Best Coding LLM for 6GB VRAM: Laptop GPU Picks (6GB VRAM)
A local Copilot on an RTX 3050 or 4050 — and the 6.7B model that looks like it fits but does not
The best local coding LLM for 6GB VRAM in 2026 is Qwen 2.5 Coder 7B at Q4_K_M — 4.36 GiB in Qwen’s own GGUF repo, and it fits because its KV cache is cheap: 28 layers with 4 KV heads costs 56 KiB per token, so an 8K code context adds only ~448MB. The obvious-looking alternative, DeepSeek-Coder 6.7B, has smaller weights (3.8GB) but no grouped-query attention at all — 32 KV heads means 512 KiB per token, nine times more, and an 8K window alone would want 4GB. For autocomplete run Qwen 2.5 Coder 1.5B (1.04 GiB) alongside a 3B, which is the only two-model setup that genuinely fits 6GB. The 14B coder needs about 9GB and is a 12GB card away.
Models that fit in 6GB VRAM
Tested reference: RTX 3050 6GB / RTX 4050 laptop / RTX 2060. Figures are for Q4_K_M with a modest context window.
| Model | Size | Build | VRAM used | Speed |
|---|---|---|---|---|
| WINNERQwen 2.5 Coder 7B (Q4_K_M) The ceiling and the right default for chat, refactors and code review. 4.68 GB on disk in Qwen/Qwen2.5-Coder-7B-Instruct-GGUF. It fits a 6GB card because of grouped-query attention: 28 layers × 2 × 4 KV heads × 128 head_dim × 2 bytes = 56 KiB/token, so 8K of context is ~448MB and 16K is ~896MB. Run it alone. ollama pull qwen2.5-coder:7b | 7B | Q4_K_M GGUF | 4.36 GiB | slowest here · ~4.7GB/token |
| Qwen 2.5 Coder 3B (Q4_K_M) The half of the two-model setup that does the thinking. 2.10 GB on disk, 36 KiB/token of KV, and it leaves enough of a 6GB card free to keep a 1.5B autocomplete model resident at the same time — which the 7B does not. ollama pull qwen2.5-coder:3b | 3B | Q4_K_M GGUF | 1.96 GiB | fast · ~1.9GB/token |
| Qwen 2.5 Coder 1.5B (Q4_K_M) The autocomplete model, and the one Continue’s own autocomplete documentation uses in its Ollama example. Fill-in-the-middle trained, 28 KiB/token of KV, and small enough that inline suggestions arrive while you are still typing. ollama pull qwen2.5-coder:1.5b | 1.5B | Q4_K_M GGUF | 1.04 GiB | fastest · ~986MB/token |
| qwen3.5:4b Not a code-specific model, but the newest thing that fits and the one to use when the job is half coding and half everything else — tool calling, thinking, a 262K native context and 55.8 on LiveCodeBench v6 in Qwen’s reported table. Lower on pure code than the 7B coder; far better at reading a stack trace and a screenshot. ollama pull qwen3.5:4b | 4B (dense, vision) | Q4_K_M + vision projector | 3.4GB | mid · ~2.7GB/token |
| DeepSeek-Coder 6.7B Listed for the fill-in-the-middle quality, with a warning attached. Its config uses 32 attention heads and 32 KV heads — full multi-head attention, no GQA — so KV costs 32 × 2 × 32 × 128 × 2 = 512 KiB per token. 3.8GB of weights plus 2GB of KV at a 4K window is already 5.8GB. Usable at a very short context, painful past it. ollama pull deepseek-coder:6.7b | 6.7B | Q4_K_M | 3.8GB | mid · ~3.8GB/token |
| codegemma:2b-code Google’s fill-in-the-middle build, 8K context, and the lightest autocomplete option if you want something other than Qwen in that slot. Code completion only — pair it with a chat model rather than talking to it. ollama pull codegemma:2b-code | 2B | Q4_0 | 1.6GB | fast · ~1.6GB/token |
What won't fit in 6GB
- ✗Qwen 2.5 Coder 14B (needs ~9GB) — The noticeably smarter step up — 8.99 GB on disk at Q4_K_M, plus 192 KiB/token of KV cache. It needs a 12GB card, and it is the reason the 12GB coding tier exists.
- ✗rnj-1:8b (needs 5.1GB) — Essential AI’s dense 8B for code and STEM. Weights alone leave under 1GB on a 6GB card, so it offloads.
- ✗ornith:9b (needs 5.6GB) — The new self-improving agentic coder. Same problem, worse: 5.6GB before context on a card that has 6.
- ✗deepseek-coder-v2:16b (needs 8.9GB) — The MoE Lite coder. Only ~2.4B parameters fire per token, but all 16B have to be resident — MoE saves compute, not VRAM.
- ✗devstral-small-2:24b (needs 15GB) — Mistral’s agentic coder. A 16GB-card model; nothing you do at 6GB gets you here.
- ✗Qwen3-Coder 30B A3B (needs 19GB) — The current long-context agentic coder from Qwen. 24GB territory.
How to fit more in 6GB
- →The "speed" column is bytes read per generated token — the resident size — not a benchmark. Decode is memory-bandwidth bound, so tok/s ≈ your card’s bandwidth ÷ that number. An RTX 4050 laptop and an RTX 2060 run this same list at very different rates, so no absolute tok/s is quoted.
- →The two-model split people recommend at 8GB does not survive at 6GB in its usual form. The 7B chat model (4.36 GiB) plus a 1.5B autocomplete model (1.04 GiB) is 5.4 GiB of weights before a single token of context. The version that works is 3B for chat + 1.5B for autocomplete — 3.0 GiB total, leaving real KV headroom.
- →Judge a coder by KV cost per token, not by parameter count. Qwen 2.5 Coder 7B costs 56 KiB/token; DeepSeek-Coder 6.7B costs 512 KiB/token for a *smaller* model, because it has no grouped-query attention. On a 6GB card that single architectural difference decides which one you can actually use with a real file open.
- →Stay at Q4_K_M for code. Q3 saves under a gigabyte and introduces the subtle syntax and off-by-one errors that cost more time than the VRAM was worth. If Q4 does not fit, drop a size class instead of a quant level.
- →Cap the editor context deliberately. Continue and Cline will happily send a whole repo; on 6GB, feed the model the open file plus the two or three it imports. 8K of context on the 7B coder is ~448MB of KV — that is your budget, and it is enough for focused work.
- →Verify after every model change with `ollama ps`. If the PROCESSOR column is not 100% GPU, your completions just got several times slower and nothing in the editor will tell you.
Quick start
ollama pull qwen2.5-coder:3b && ollama pull qwen2.5-coder:1.5bollama run qwen2.5-coder:7bname: Coder chat | provider: ollama | model: qwen2.5-coder:3b | roles: [chat, edit, apply]name: Coder autocomplete | provider: ollama | model: qwen2.5-coder:1.5b | roles: [autocomplete]ollama ps # PROCESSOR must read 100% GPUGo from "it runs" to actually building
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Frequently asked questions
What is the best local coding model for a 6GB laptop GPU?
Qwen 2.5 Coder 7B at Q4_K_M. It is 4.36 GiB of weights and only 56 KiB per token of KV cache, so an 8K code context costs about 448MB and the whole thing stays on a 6GB card. Run it on its own; if you also want inline autocomplete, drop to the 3B and pair it with the 1.5B.
Can I run a local Copilot on 6GB VRAM?
Yes, with the right pairing. Pull qwen2.5-coder:3b for chat and edits and qwen2.5-coder:1.5b for inline completion — 3.0 GiB of weights together — then point Continue at both with roles: [chat, edit, apply] and roles: [autocomplete]. Continue’s own autocomplete docs use the same 1.5B model in their Ollama example.
Why is DeepSeek-Coder 6.7B a worse fit than the bigger Qwen 7B?
Attention layout. Qwen 2.5 Coder 7B uses grouped-query attention with 4 KV heads, so its cache is 28 × 2 × 4 × 128 × 2 = 56 KiB per token. DeepSeek-Coder 6.7B has 32 KV heads — no GQA — which works out at 512 KiB per token. Its weights are ~0.5GB smaller and its context is nine times more expensive, and on a 6GB card context is the scarce resource.
Is there a newer small coder than Qwen 2.5 Coder?
Not at this size, as of August 2026. Every coding model released since — Qwen3-Coder 30B A3B, Devstral Small 2 24B, North Mini Code 30B, the Kimi K2.7 code line — starts at 24B or above, and the small end of the Ollama library is still qwen2.5-coder, codegemma and deepseek-coder. That is why an older model wins this tier: nothing newer has been built for it.
Does the 14B coder fit 6GB at a lower quant?
No, and it is not close. Qwen 2.5 Coder 14B is 8.99 GB at Q4_K_M — more than the card holds before you add its 192 KiB/token KV cache. Squeezing it to 3-bit would fit the weights and destroy exactly the syntactic precision you wanted the coder for. The 14B is the 12GB tier.
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