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There is no single best local coding model for every GPU. The practical choice is the strongest model that fits your card with enough memory left for context, the inference runtime, and the rest of your coding setup. These 8GB, 16GB, and 24GB options are starting points—not results from a controlled, same-hardware comparison.
Which local coding model fits your VRAM?
Start with your available GPU memory, then test the model artifact you intend to run. The figures below are publisher-reported Q4 weight or artifact estimates, not guarantees of total runtime memory use.
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| Available VRAM | Candidates to try | What to expect |
|---|---|---|
| 8GB | Qwen2.5-Coder 7B; StarCoder2 7B | Local AI Models estimates Q4 weights at about 4.6GB for Qwen2.5-Coder 7B and 4.2GB for StarCoder2 7B. These smaller models are more realistic for completion and straightforward code tasks than a larger checkpoint, but the estimates exclude the full runtime and context-cache budget. Local AI Models |
| 16GB | DeepSeek-Coder-V2-Lite 16B; Devstral 2 22B | Local AI Models estimates about 9.6GB of Q4 weights for DeepSeek-Coder-V2-Lite 16B. LLM Configurator lists a roughly 14.1GB Q4 artifact for Devstral 2 22B. These are separate sizing estimates, not a head-to-head comparison, and the Devstral figure leaves less room for runtime and context on a 16GB card. Local AI Models; LLM Configurator |
| 24GB | Qwen3-Coder-30B-A3B-Instruct | Local AI Models reports 30.5B total parameters, 3.3B active parameters, and roughly 18GB of Q4 weights. That makes it a candidate to test on a 24GB card, not a promise that every runtime and context setting will fit. Local AI Models |
Ollama’s coding-model catalog also lists Qwen3-Coder, Qwen2.5-Coder, DeepSeek-Coder, DeepSeek-Coder-V2, and OpenCoder in multiple sizes. Catalog availability identifies candidates; it does not establish their coding quality or suitability for your hardware. Ollama model library
Why a model’s weight size is not its full VRAM requirement
GPU memory must accommodate more than model weights. The inference runtime and the key-value (KV) cache for the active context also use memory, as can another model or application sharing the GPU. Q4 quantization reduces weight storage, but the cited numbers are estimates and can differ from the exact artifact you download.
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The KV cache grows with context. A model that loads at 8K tokens may not fit—or may run unacceptably slowly—at 32K. Coding-agent loops can add tool output, prompts, and source files to the context, so a published maximum context length is not a guarantee that you can use the entire window on your card.
WhatLLM.org recommends reserving roughly 15–25% of memory as headroom and starting with 16K or 32K context, increasing it only if repository retrieval needs more. Treat that as a practical recommendation, not a universal measured threshold. WhatLLM.org
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Choose based on the work you need the model to do
A model that can load is not necessarily the right model for your workflow. Compare candidates that fit on the same machine against the tasks you actually perform:
- Task type: Separate autocomplete and single-file help from multi-file, agentic changes. A result on one kind of task does not establish performance on another.
- Usable context: Measure how much context remains after allocating for weights, runtime, and cache—not just the model’s advertised maximum.
- Latency and throughput: Decide what response speed is acceptable on your hardware; the sizing guides do not provide a consistent same-machine speed comparison.
- Reliability: Try real repository issues, including cases where the model must diagnose a failed change or recover from a mistake. Keep track of errors as well as successful edits.
- License: Check the exact release’s terms before commercial deployment. The cited guide lists Qwen3-Coder and Devstral Small 2 as Apache 2.0, while DeepSeek-Coder-V2 weights use DeepSeek’s model license; StarCoder2 and Codestral also have their own restrictions. Confirm the terms for the specific artifact you plan to use. Local AI Models
Do not turn published benchmark scores into a cross-model ranking unless the hardware, task set, and evaluation harness match. The cited pages warn that some SWE-bench scores come from different publishers and harnesses, so those figures are not directly comparable.
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A practical way to test candidates on your own machine
- Check the exact artifact size. A model name alone does not specify the quantization or file size. Confirm the artifact you will run and compare its size with your available VRAM.
- Leave room for the rest of the workload. Account for the runtime, context cache, and other GPU applications. Do not assume all VRAM can be assigned to weights.
- Begin with a moderate context. Try 16K or 32K as a starting point, then raise it only when your repository tasks need more and memory and latency remain acceptable.
- Run it through your actual editor or coding agent. Test representative tasks in the setup you intend to use; a model that loads in isolation may behave differently when tools and repository files are added.
- Keep a small private evaluation set. Use real issues from your repositories and record quality, latency, failures, and recovery. Compare only results produced under consistent conditions.
- Keep an escalation path. If a task is too difficult or the local model cannot fit, move that task to a model or compute setup suited to it rather than treating one local model as the answer to every coding problem.
What changes between 8GB, 16GB, and 24GB?
8GB: prioritize a small coding model
At this capacity, start by testing Qwen2.5-Coder 7B or StarCoder2 7B. Their estimated Q4 weight sizes leave more room for the runtime and context than a larger model would, though actual usable context depends on the artifact and the rest of the system. Keep expectations focused on completions and simpler coding assistance.
16GB: larger options need careful fit checks
DeepSeek-Coder-V2-Lite 16B and Devstral 2 22B illustrate why checking the exact artifact matters: the cited Q4 estimates are about 9.6GB and 14.1GB, respectively. The larger estimate would leave little margin on a 16GB card once context and runtime are included. These figures do not establish which model produces better code.
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24GB: more room for larger quantized candidates
A 24GB card makes larger coding models more practical to evaluate, but it does not remove the need to budget for context and runtime. Qwen3-Coder-30B-A3B-Instruct is reported at roughly 18GB of Q4 weights. Although it has 3.3B active parameters per token, it has 30.5B total parameters; active-parameter count should not be mistaken for the total weights that need to be stored.
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