Hardware for a Local Coding Agent: What Needs to Be Always On?

The confusing part of a “local coding agent” build is the word local. It can mean the agent interface runs on your desktop, the model runs on a GPU in another room, or both the code and inference stay on hardware you control. Those are different setups with different hardware needs.

Real user questions reflect that tangle: people ask which agent works with their local model server, why a setup fails despite answering chat prompts, and whether an 8 GB GPU can handle multi-step coding work. A separate thread asks what model, backend, and context fit an 8 GB card for real project work. Those are useful problem statements, not controlled hardware tests or evidence that one product is best (agent + local setup question, hardware and model-fit question, both accessed 2026-09-24).

The buying decision is therefore which parts must stay available, where inference runs, and what task quality and delay are acceptable. For general runtime tradeoffs, see Ollama vs llama.cpp vs vLLM. For a hardware-first model-fit framework, see The Local-AI Hardware Buying Framework.

Split the always-on part from the model

A coding-agent setup has at least two jobs:

1. Orchestration: the IDE or terminal integration, task queue, gateway, repository checkout, and tool permissions.
2. Inference: loading the language model and processing prompts, code, tool results, and conversation context.

They can share one machine, but they do not have to. A small, low-power host can keep a gateway or automation service reachable while sending model requests to a workstation, a model server that wakes when needed, or a cloud API. Continue’s Ollama provider page, fetched 2026-10-06, shows an apiBase field for a remote machine serving Ollama. That is a software integration option. It is a claim that a remote address can be configured, and it leaves security and reliability for you to check on that host.

This separation matters for “always-on.” If you want to message an agent at any hour, some orchestration endpoint must be reachable. It does not follow that a large GPU must sit at full power around the clock. If you require an immediate local answer with no wake-up delay and no external service, then the inference machine must also be available. If a few seconds to wake a desktop is acceptable, a scheduled or on-demand inference host may suit better. The watt table below is for the case where the inference machine itself stays powered on.

Size the inference machine for the model and context

There is no reliable one-line VRAM minimum for agentic coding. A model’s weights are only part of the memory budget. The configured context, runtime overhead, operating system, IDE, and other loaded applications also need room. A longer context generally consumes more memory. Reducing the context can resolve some out-of-memory errors, and it can also leave the agent with less repository history or fewer tool results in view.

Ollama’s launch note, published 2026-01-23 and fetched 2026-10-06, says coding tools work best with a full context length and tells you to set context to at least 64,000 tokens. The same note’s example command lists about 23 GB of VRAM for glm-4.7-flash at 64,000 tokens. That is a vendor-published requirement for one named model and context. It is not a blanket requirement for every coding agent, and it is not a LocalRig benchmark. Continue’s Ollama guide, fetched the same day, says tool support is required for Agent mode, and that some models which claim tool support still fail. Check the model tag, runtime, quantization, context setting, and tool-call support before buying hardware.

Practical implications:

• Small GPU or CPU-only machine: treat it as a trial platform for small models, autocomplete, or a remote-model gateway. If inference is slow or tools fail, first separate model limitations from a broken integration.
• Midrange GPU: choose a specific model and target context, then verify its memory footprint with the runtime’s own status tools. Leave room for the desktop and other workloads. The model file’s disk size is a different number from peak VRAM use.
• Larger VRAM GPU or high-memory unified-memory system: this opens more model and context choices. It does not guarantee good code edits, dependable tool calls, or fast end-to-end task completion. Test the exact model-agent pairing on representative repository work.
• Separate always-on gateway and inference workstation: useful when availability matters and keeping the larger system awake does not. The tradeoff is another network boundary and possible wake latency. Secure the endpoint, and keep an unauthenticated model API off the public internet.

Flint, released 21 October 2026

Recursive Flint is a coding model with its own fit page: Can I run Recursive Flint locally?. The model card, created 2026-10-21 and read 2026-10-22, lists 39.8B total parameters, about 2.5B active per token, a 262,144-token context, and an Apache 2.0 license. The file, not the parameter count, decides the GPU.

Checked against the Hugging Face tree API on 2026-10-22:

• Unsloth’s 3-bit UD-Q3_K_XL is 18.49 GB. Their guide puts that file on 24 GB at short context. The example context in that guide is 8,192 tokens.
• ggml-org’s Q4_K_M is 23.39 GB. That file is inside a 24 GB card with almost no spare for KV.
• bartowski’s Q4_K_M is 25.35 GB. A 24 GB card does not hold it. lmstudio-community’s Q4_K_M is a third size, 24.17 GB.
• Recursive’s own full-context llama.cpp commands name 48 GB. A weights fit is not a filled 262,144-token context. This page has no Flint KV measurement.

Ollama v0.40.8, published 2026-10-21, documents ollama run flint and says macOS support, with other GPUs in a later release. The library page on 2026-10-22 lists flint:40b at 23 GB and 256K, and says quantized builds exist for Apple Silicon and other GPUs. Those two Ollama sentences do not match. llama.cpp tag b11803, also 2026-10-21, adds the architecture and is marked pre-release.

Speeds, each labeled:

• Single source: llm-speed r_e8r7fxw1ke9, measured 2026-10-22, RTX A4000 16 GB, IQ3_XXS, llama.cpp build 11803. Chat-short decode is 92.89 tok/s. Agent-trace decode on the same page is 84.71 tok/s. The page does not name the GGUF repo.
• Vendor claim: Richard Socher, 2026-10-21, says 125+ tok/s on a MacBook Pro and 150+ on RTX Spark. Unsloth’s guide says 125+ on an M4 Pro MacBook Pro and 150+ on a 64 GB NVIDIA RTX Spark. Neither sentence names a quant.
• Single source, the publisher’s card: TandemLLM’s EXL3 6.0 pack, on one DGX Spark (GB10, 128 GB), plain greedy decode from 94.3 to 98.4 tok/s on the six tasks that card names. That is not the 150+ vendor line.

Running Flint does not change the watt table below. Those rows were fetched 2026-10-06 and were not remeasured for this update. The RTX 3090 sold-price median on this page stays the 2026-10-06 CompSniper reading. priceDate is 2026-10-06. dataDate is 2026-10-22 because that is when the Flint file sizes and the A4000 run were checked.

Machines meant to run a coding agent all day

An all-day agent box is a different purchase from a card you turn on for a chat. Four things decide whether the machine is actually usable for that job.

Prompt-processing speed on long contexts. Each agent turn sends a long prompt: repository files, tool output, and the conversation so far. The wait before the first new token is prompt processing (prefill). A decode figure from a short chat is a different measurement. This page does not have a prefill tokens-per-second cite packet — model, quantization, runtime, context, and hardware together — for a coding-agent trace on the classes below. That cell is not yet measured.

What is published, and labeled as vendor, is Apple’s comparison against older Macs. The 25 August 2026 newsroom post says testing was conducted by Apple in July 2026. It says Mac Studio with M5 Max is up to 10.7× faster at LLM prompt processing in LM Studio than Mac Studio with M1 Max, and 3.9× faster than M4 Max. It says M5 Ultra is up to 9.8× faster than M1 Ultra, and up to 4× faster than M3 Ultra. Those are vendor multiples. The sentences do not name the model, the quantization, or the context length. They are not a LocalRig result, and they are not a comparison against an NVIDIA card.

Context length that fits. Ollama’s 64,000-token floor, with the glm-4.7-flash example at about 23 GB, is the concrete vendor marker for a single 24 GB card: that one example sits on the edge of the card. A 32 GB card, a second GPU, or a 128 GB unified-memory machine is the step when the working set is larger than that example. How many tokens of a named coding model fit on each class, after the operating system, is not yet measured on this page. Size a specific file in the VRAM calculator and then confirm it in the runtime. The calculator is an estimate.

Noise and thermals. A machine that runs for a workday has to stay inside a power and temperature limit you can live with at a desk. The dated noise figures below are tied to the test that produced them. An LLM fan measurement is a different test, and several classes do not have one.

Reliability. This page has no dated failure-rate study for these machines under agent load. What the sources do support is the power and heat the hardware is specified or measured to produce, and the PSU recommendation the vendor printed for a named example PC. A supply sized only to a GPU’s TDP number, with nothing left for the rest of the computer, is the usual way a long job falls over. The PSU guide is the longer version of that planning step.

Single 24–32 GB GPU

This class is a desktop with one consumer NVIDIA card in the 24 GB or 32 GB generation. The two ends of that range have different vendor power numbers.

The RTX 3090 spec table, fetched 2026-10-06, lists the GeForce RTX 3090 (the second column, 10,496 CUDA cores) at 24 GB GDDR6X and 350 W graphics card power. The first column is the 3090 Ti at 450 W. Footnote 4 says the 750 W “required system power” figure is based on a PC with an Intel Core i9-10900K, and that a lower rating may work depending on the system. That 750 W line is a vendor PSU recommendation for an example PC. It is not a measured wall reading.

Andrew Burnes, NVIDIA GeForce News, 15 November 2022, published a card-power table that lists the RTX 3090 at 13 W idle, 345 W average gaming, and 350 W TGP. Those are vendor card figures. Average gaming is a game workload. It is not an LLM agent trace, and it is not whole-PC wall power. Whole-PC idle and whole-PC load for a single-3090 coding desktop are not yet measured here.

The RTX 5090 spec table, fetched 2026-10-06, lists 32 GB GDDR7 and 575 W total graphics power. Footnote 5 says the 1000 W minimum system power is based on a PC with a Ryzen 9 9950X, and that power requirements can differ with the system. That is a vendor PSU recommendation. Card idle, LLM wall power, and whole-PC wall power for a 5090 coding desktop are not yet measured in a sentence this page can quote. TechPowerUp’s Founders Edition review (W1zzard, 23 January 2025) says its power page is card-only DC measurement, and the watt values sit in charts. This page does not transcribe a chart into a watt.

A used RTX 3090 is the usual way into the 24 GB end of this class. CompSniper, read 2026-10-06, reports a median of $1,499 across the 204 most recent completed eBay sales, with most sales from $1,329 to $1,599. The most recent sale in that sample was 6 October 2026. The buying checklist is the used RTX 3090 guide.

Used multi-GPU rigs

Two used RTX 3090s are the concrete multi-GPU case: 24 GB + 24 GB of card memory when the runtime can use both. The dual RTX 3090 build guide is the assembly page.

Vendor arithmetic from the spec table above: 350 W + 350 W = 700 W of graphics-card power if both cards sit at the 3090’s listed graphics card power. That sum is arithmetic on a vendor spec. It is not a measured wall reading of a dual-GPU computer, and it excludes the CPU, board, drives, fans, and power-supply losses. Doubling the 13 W single-card idle figure would invent a dual-card idle measurement, so this page does not do that. Dual-rig idle wall power and dual-rig load wall power are not yet measured.

Two cards at the CompSniper median are 2 × $1,499 = $2,998 before tax, shipping, and the rest of the computer. That is arithmetic on the single-card median. It is not a sold price for a pair.

Apple Silicon M5 family

Mac Studio with M5 Max and M5 Ultra is the current Apple desktop in this family. Memory is chosen at purchase. The configuration guide, including which unified-memory step holds which already-published model class, is M5 Max and M5 Ultra. M5 decode tokens per second are not yet measured on that page, and they are not yet measured here.

Apple Support’s power table, published 21 September 2026, measures wall power with no external peripherals. “Max” is a compute-intensive test application. “Idle” is Finder only, at about 20.2°C. The notes say the watts include power-supply and system losses:

• M5 Max, 18-core CPU, 32-core GPU, 36 GB, 512 GB SSD: 7 W idle, 200 W max.
• M5 Ultra, 36-core CPU, 80-core GPU, 512 GB memory, 16 TB SSD: 9 W idle, 385 W max.

Those two configurations are the ones in the table. A 128 GB M5 Max, and the other memory steps, are not yet measured for wall power. LLM watts on any M5 configuration are not yet measured. The 200 W and 385 W figures are Apple’s max test, which is a different workload from a coding agent.

Noise, from Apple’s UK M5 tech-spec page, fetched 2026-10-06: the configuration tested is an M5 Ultra, 30-core CPU, 64-core GPU, 96 GB, 1 TB. Declared sound pressure at the operator position is 7 dB at idle and 7 dB on the wireless-web test (sound power 1.5 B, Kv = 0.3). Wireless web is six browser tabs with mixed content, including media. It is not a saturated inference load. LLM fan noise on M5 is not yet measured.

Strix Halo 128 GB boxes

Strix Halo here means a desktop or mini PC built on AMD’s Ryzen AI Max+ 395, sold with a large unified-memory pool. The field of 128 GB boxes is the mini PC guide. AMD’s Ryzen AI Max+ 395 page, fetched 2026-10-06, lists a default TDP of 55 W and a configurable TDP of 45–120 W. That is a chip spec. It is not wall power, and a desktop that raises the chip to the top of that range is a different operating point from the 55 W default.

Two review measurements exist for this chip in a desktop, and they are different tests:

• Allen Ngo, Notebookcheck, published 20 September 2025, measured a Framework Desktop with a Ryzen AI Max+ 395 and an external monitor. Idle for one minute averaged 10.5 W (range 9.64–15.3 W). Prime95 averaged about 144 W. Prime95 is a CPU stress test. The article does not state the memory size of that unit in the power comparison, so this page does not label it as the 128 GB SKU.
• Jeffrey Kampman, Tom’s Hardware, published 27 January 2026, wrote that their Strix Halo system consumes about 112 W under an LLM inference load. The same review describes Strix Halo as a way to get 128 GB of RAM. The power sentence does not name the memory size of the metered unit, and it does not give that unit’s idle watts. Idle for that system is not yet measured in the review text.

Wall power for the other 128 GB chassis (Beelink, GMKtec, Minisforum, and the rest of that guide) is not yet measured on this page. A decibel figure for the Framework Desktop under an agent load is not yet measured in the Notebookcheck article.

DGX Spark

NVIDIA’s DGX Spark is the small Grace Blackwell box. The 64 GB versus 128 GB page is the memory and price split. The product page, fetched 2026-10-06, lists a 240 W power supply and a 140 W GB10 TDP on the spec table. The product description in that fetch includes 128 GB LPDDR5x. MackenzieNVIDIA, on the NVIDIA Developer Forums, 31 October 2025, states the peak total system power is 240 W, the GB10 SoC TDP (GPU and CPU) is 140 W, and the rest of the system (ConnectX-7, SSD, and USB-C provision) is 100 W. That is a vendor power budget. A separate wall measurement of the 64 GB SKU versus the 128 GB SKU is not yet measured.

Review wall readings, which disagree because the workloads differ:

• Tom’s Hardware, 27 January 2026: idle about 35 W headless, about 40 W with a 4K 160 Hz panel, and about 160 W at the wall under a primarily GPU-driven load. Under an extended image-generation load, the loudest noise was 37.5 dBA at 18 inches (their room’s noise floor was 33 dBA). nvtop showed the GB10 GPU staying at or under 82°C under sustained load.
• Patrick Kennedy, ServeTheHome, 14 October 2025: idle in the 40–45 W range. LLM inference workloads used 60–90 W, and the system stayed under 40 dBA at 1–1.5 m when it was not being stress-tested. CPU load reached 120–130 W. Adding the GPU and other components reached just under 200 W and did not reach 240 W. “Just under 200 W” is the review’s wording. This page does not turn that phrase into a dollar figure.

The 60–90 W ServeTheHome band is the review figure closest to an LLM workload. The 160 W Tom’s Hardware figure is a heavier GPU-driven load. Both stay in the table as separate rows.

Idle, load, and the monthly power bill

The rate is the U.S. residential average from EIA Electric Power Monthly, Table 5.6.A, July 2026: 18.31 cents per kWh ($0.1831). The page was fetched 2026-10-06. EIA marks the table preliminary. Your utility bill can be far from a national average. The rent versus buy break-even is where that rate, your hours, and a purchase price go into an ownership comparison against renting. The interactive version is the local-versus-cloud calculator.

The schedule, stated so the dollars can be recomputed:

• Load: 8 hours a day × 30 days = 240 hours a month.
• Idle: 16 hours a day × 30 days = 480 hours a month.
• The machine is left powered on all month. The 8 hours are a workday of the load named in that row.

Dollars = (watts ÷ 1000) × hours × 0.1831, rounded half-up to the cent. The month column uses both hour blocks in one product, then rounds once. Adding the rounded idle and load columns can differ by a cent. Where a row has only one watt figure, the other part of the month is not yet measured, and the month cell says so.

Vendor TDP, TGP, and graphics-card power are vendor spec. Apple’s wall table and the review watts are measurements, and the review rows are community or press measurements, not LocalRig. A card-only watt excludes the rest of the PC.

Class and source	Idle	Load	What the watts are	Idle / month	Load / month	Month, 8h + 16h
RTX 3090 card. NVIDIA GeForce News, 2022-11-15	13 W	345 W	Vendor card measurement. Idle at the card. Load is average gaming, not an LLM	$1.14	$15.16	$16.30
RTX 3090 card at the spec cap. Same table, plus the spec page fetched 2026-10-06	13 W	350 W	350 W is vendor graphics-card power / TGP. Still the card, not the PC	$1.14	$15.38	$16.52
RTX 5090. Spec page fetched 2026-10-06	not yet measured	575 W	Vendor total graphics power. Card spec, not wall, not an LLM	not yet measured	$25.27	not yet measured
Two RTX 3090s at the spec cap	not yet measured	700 W	Arithmetic of two 350 W vendor graphics-card-power figures. Not a wall reading	not yet measured	$30.76	not yet measured
Mac Studio M5 Max, named config. Apple Support, 2026-09-21	7 W	200 W	Vendor wall measurement. Finder idle. Max is a compute test, not an LLM	$0.62	$8.79	$9.40
Mac Studio M5 Ultra, named config. Same Apple table	9 W	385 W	Same protocol, on the 36-core / 80-core / 512 GB / 16 TB configuration	$0.79	$16.92	$17.71
Other M5 memory tiers, including 128 GB M5 Max	not yet measured	not yet measured	Not in Apple’s power table	not yet measured	not yet measured	not yet measured
Framework Desktop, Ryzen AI Max+ 395. Notebookcheck, 2025-09-20	10.5 W	about 144 W	Review wall measurement, external monitor. Idle is a 1-minute average. Load is Prime95, not an LLM. Memory size not stated	$0.92	$6.33	$7.25
Tom’s Hardware Strix Halo system, 2026-01-27	not yet measured	about 112 W	Review wall measurement under an LLM inference load. Memory size of the metered unit not stated	not yet measured	$4.92	not yet measured
Ryzen AI Max+ 395 chip at the top of cTDP. AMD page, fetched 2026-10-06	not yet measured	120 W	Vendor configurable TDP. Chip spec, not wall	not yet measured	$5.27	not yet measured
DGX Spark. Tom’s Hardware, 2026-01-27, headless idle	about 35 W	about 160 W	Review wall measurement. Load is primarily GPU-driven, not the ServeTheHome LLM band	$3.08	$7.03	$10.11
DGX Spark. Same review, panel connected	about 40 W	about 160 W	Idle with a 4K 160 Hz panel. Same GPU-driven load	$3.52	$7.03	$10.55
DGX Spark. ServeTheHome, 2025-10-14, low end of both ranges	40 W	60 W	Review wall measurement. Idle range starts at 40 W. LLM workloads start at 60 W	$3.52	$2.64	$6.15
DGX Spark. Same review, high end of both ranges	45 W	90 W	High end of the idle range and of the LLM-workload range	$3.95	$3.95	$7.91
DGX Spark vendor peak budget. Product page fetched 2026-10-06; forum post 2025-10-31	not yet measured	240 W	Vendor peak system budget (140 W GB10 SoC TDP + 100 W for the rest). Not a measured agent load	not yet measured	$10.55	not yet measured

The whole-PC bill for a single 3090 or 5090 desktop is the open cell. The card-only month of about $16 for a 3090, or the $25 load portion for a 5090, leaves out the CPU, board, drives, and power-supply losses for every one of those 240 and 480 hours. Treat those rows as a floor on the GPU’s share.

On the machines with a measured wall figure, a month of this schedule is a small bill next to the purchase price: about $6.15–$7.91 for the ServeTheHome Spark LLM band, about $10.11–$10.55 for Tom’s Hardware Spark GPU-load figure, $7.25 for the Framework Prime95 row, $9.40 for the published M5 Max row, and $17.71 for the published M5 Ultra row. The M5 “load” is Apple’s max compute test. An agent that uses less than that test draws less than the load column. This page does not have that agent measurement, so it does not shrink the number.

Test the agent, not just chat

A model that answers “write a function” in a chat window has not yet passed an agent test. An agent has to follow the harness’s tool format, inspect files, choose actions, handle tool output, and recover from errors over multiple turns. Continue’s Ollama guide, fetched 2026-10-06, says tool use is required for Agent mode and describes cases where advertised support does not work. A failure can come from the model, quantization, context configuration, runtime, or adapter.

Before upgrading, test three tasks in a disposable copy of a real repository: a small bug fix, a multi-file change with tests, and a task that requires the agent to inspect unfamiliar code. For each setup, record whether it completed, whether its tool calls parsed, how much human correction was needed, end-to-end time, and peak memory. Review every diff. Keep an unattended agent away from write access on a valuable repository. If the small task fails at tool-call formatting, a larger GPU is the wrong next purchase.

When local inference makes sense

Local inference fits when keeping prompts and code on your own machine is a priority, you need offline operation, you already own hardware that can run the model, or you have steady enough usage to justify maintaining the host. A local model does not by itself prove that every part of the workflow stays on-device. Check the agent’s telemetry and connected services.

The cost comparison should include more than the model API bill. For local use, include the purchase or opportunity cost of hardware, power while loaded and idle, cooling, storage, setup, maintenance, and the value of your time. The watt table is the power term, at one stated rate and one stated schedule. For cloud use, check current pricing, usage limits, retention terms, network dependence, and the cost of repeated agent calls. A machine you already own has a different break-even point from a new GPU bought for occasional experiments. There is no honest universal “local is cheaper” threshold without your hours and your electricity price.

Readers weighing a cloud API instead of one of these machines should use the rent versus buy break-even. That page is the ownership model: purchase price, electricity, depreciation, and the rental side. Put your own hours into the calculator rather than treating either article’s example schedule as your usage.

When cloud, or a hybrid, is the better fit

Use cloud inference when your local model cannot complete the work reliably, when you need a larger context or stronger capability than your machine can support, or when occasional usage does not justify buying and maintaining hardware. Cloud also removes the need to keep a suitable inference host available. The orchestration environment and credentials still need care.

A hybrid arrangement is often the least-regret starting point: a local model for bounded, private, or routine tasks, and a cloud model for difficult work after you have checked what code and prompts are sent. A local workstation can also serve several clients on a trusted home network while a small gateway remains available. In either case, start with narrow permissions, inspect proposed changes, and keep secrets out of prompts and test repositories.

A decision rule

Your priority	Start here	Hardware decision
Keep an agent endpoint reachable, with occasional coding	Separate gateway from inference	Keep the gateway modest. Wake or remotely call the model host when needed
Private or offline coding on hardware already owned	Local model plus a tool-compatible agent	Test one model, quant, runtime, and context before buying anything
A machine that stays on for a workday	One of the classes above, after the context fits	Use a row whose watts are measured at the wall. Leave empty cells empty
Best results on difficult code tasks, used occasionally	Cloud model, or a local/cloud hybrid	Compare current service terms with the rent versus buy break-even
Several concurrent users or jobs	Treat it as a serving workload	Benchmark concurrency and memory under the intended load before choosing a server

The least-regret purchase is usually the one you can postpone until a representative task fails for a known reason. First confirm that the agent, model, and runtime can exchange tool calls. Then measure memory and task completion at the context you actually need. Buy for that measured constraint.

Who this is NOT for

• People who need a model recommendation without a task, a repository size, a context length, a privacy requirement, and their current hardware.
• Buyers expecting an 8 GB GPU, or any fixed VRAM tier, to guarantee dependable autonomous coding.
• Teams looking for a production multi-user inference-server capacity plan. That needs a defined concurrency target and a load test.
• Anyone who wants an unattended agent to modify important code without review, backups, or scoped permissions.
• Anyone who needs a whole-PC watt figure for a single 3090 or 5090 desktop, a dual-3090 wall reading, or an M5 LLM watt, before they will trust a power bill. Those cells are not yet measured. Guessing them would make the table look finished.

Sources

Vendor documentation supports the integration and configuration statements. It does not establish independent coding quality. The Reddit links document questions people asked. They are not a benchmark. Hardware behavior varies by model revision, quantization, runtime, context, and host. Recheck each vendor’s current instructions before you buy.

• Ollama, “ollama launch,” published 23 January 2026, fetched 6 October 2026. At least 64,000 tokens for coding tools. glm-4.7-flash listed at about 23 GB VRAM with 64,000 tokens of context. https://ollama.com/blog/launch
• Continue docs, “Using Ollama with Continue,” fetched 6 October 2026. Tool support is required for Agent mode. Some models that claim tool support still fail. https://docs.continue.dev/guides/ollama-guide
• Continue docs, Ollama provider, fetched 6 October 2026. apiBase for a remote Ollama instance. https://docs.continue.dev/customize/model-providers/top-level/ollama
• NVIDIA, GeForce RTX 3090 and 3090 Ti spec table, fetched 6 October 2026. RTX 3090 column: 24 GB GDDR6X, 350 W graphics card power, 750 W required system power for an example PC with a Core i9-10900K. https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
• Andrew Burnes, NVIDIA GeForce News, 15 November 2022. RTX 3090 card figures: 13 W idle, 345 W average gaming, 350 W TGP. https://www.nvidia.com/en-us/geforce/news/geforce-rtx-40-series-ultra-efficient-beyond-fast/
• NVIDIA, GeForce RTX 5090 spec table, fetched 6 October 2026. 32 GB GDDR7, 575 W total graphics power, 1000 W minimum system power for an example PC with a Ryzen 9 9950X. https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
• W1zzard, TechPowerUp, RTX 5090 Founders Edition review, 23 January 2025. Power page states card-only DC measurement. Watt values are in charts and are not transcribed here. https://www.techpowerup.com/review/nvidia-geforce-rtx-5090-founders-edition/43.html
• CompSniper, RTX 3090 eBay sold prices, read 6 October 2026. Median $1,499 across 204 sales, typical range $1,329–$1,599, most recent sale 6 October 2026. https://compsniper.com/worth/rtx-3090
• Apple Support, “Mac Studio power consumption and thermal output,” published 21 September 2026. https://support.apple.com/en-us/102027
• Apple Support (UK), Mac Studio (M5 Max or M5 Ultra) tech specs, acoustic table, fetched 6 October 2026. Configuration tested: M5 Ultra, 30-core CPU, 64-core GPU, 96 GB, 1 TB. https://support.apple.com/en-gb/128107
• Apple Newsroom, 25 August 2026, Mac Studio with M5 Max and M5 Ultra. Vendor LM Studio prompt-processing multiples. Testing conducted by Apple in July 2026. https://www.apple.com/newsroom/2026/08/apple-introduces-new-mac-studio-with-m5-max-and-m5-ultra/
• AMD, Ryzen AI Max+ 395, fetched 6 October 2026. Default TDP 55 W, configurable TDP 45–120 W. https://www.amd.com/en/products/processors/laptop/ryzen/ai-300-series/amd-ryzen-ai-max-plus-395.html
• Allen Ngo, Notebookcheck, published 20 September 2025. Framework Desktop wall power with an external monitor. https://www.notebookcheck.net/Framework-Desktop-runs-more-efficiently-than-the-GMK-EVO-X2-with-the-exact-same-Ryzen-AI-Max-395-processor.1116958.0.html
• Jeffrey Kampman, Tom’s Hardware, published 27 January 2026. DGX Spark and Strix Halo power, noise, and temperature. https://www.tomshardware.com/pc-components/gpus/nvidia-dgx-spark-review/4
• NVIDIA DGX Spark product page, fetched 6 October 2026. 240 W power supply, 140 W GB10 TDP. https://www.nvidia.com/en-us/products/workstations/dgx-spark/
• MackenzieNVIDIA, NVIDIA Developer Forums, 31 October 2025. Peak system budget 240 W, GB10 SoC TDP 140 W, remainder 100 W. https://forums.developer.nvidia.com/t/dgx-spark-power-clarification/349668
• Patrick Kennedy, ServeTheHome, 14 October 2025. DGX Spark idle, LLM-workload, and stress wall power, plus a noise note. https://www.servethehome.com/nvidia-dgx-spark-review-the-gb10-machine-is-so-freaking-cool/4/
• U.S. Energy Information Administration, Electric Power Monthly, Table 5.6.A, July 2026 and 2025. U.S. Total residential 18.31 cents per kWh. Fetched 6 October 2026. https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a
• Community question, r/LocalLLM, accessed 24 September 2026. Anecdotal, not a benchmark. https://www.reddit.com/r/LocalLLM/comments/1ufre55/what_agent_do_you_use_with_a_local_setup_for/
• Community question, r/LocalLLM, accessed 24 September 2026. Anecdotal, not a benchmark. https://www.reddit.com/r/LocalLLM/comments/1spgiqf/best_local_codingagent_model_for_my_setup_web_dev/
• Recursive, Flint model card, created 21 October 2026, fetched 22 October 2026. 39.8B total, about 2.5B active, 262,144-token context, Apache 2.0. https://huggingface.co/RecursiveSI/flint
• LocalRig, Can I run Recursive Flint locally?, 22 October 2026. File sizes from the Hugging Face tree API that day. https://localrig.xyz/can-i-run/can-i-run-recursive-flint-locally/
• llm-speed, run r_e8r7fxw1ke9, measured 22 October 2026. RTX A4000 16 GB, IQ3_XXS, llama.cpp build 11803, chat-short 92.89 tok/s. Single source. https://llm-speed.com/r/r_e8r7fxw1ke9
• Richard Socher, 21 October 2026. Vendor claim: 125+ tok/s on a MacBook Pro, 150+ on RTX Spark. https://x.com/RichardSocher/status/2112944385415426249