Best GPU for Qwen3-Coder in 2026 (80B MoE, 256K Ranked)

Cross-posted from Best GPU for LLM — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

If you're building a local coding agent stack on top of Qwen3-Coder-Next, the GPU question changes shape. The headline number — 80B parameters — looks intimidating until you realize only 3B are active per token. That single architectural detail flips this from a multi-GPU project into something a 24GB card handles comfortably, and it's the reason Qwen3-Coder hit #1 on SWE-rebench in June and showed up as the default backend in Continue.dev, Cline, Aider, and Roo Code templates almost overnight.

Quick answer: The RTX 4090 (24GB, ~$1,600) is the consumer sweet spot for local Qwen3-Coder-Next inference. It runs the 80B MoE at Q4 with a usable 32K-128K coding context at roughly 85-110 tok/s — fast enough to keep an autonomous coding loop feeling interactive instead of batch.

See the recommended pick on the original guide

Who this is for

You're a developer wiring Continue.dev, Cline, Aider, or Roo Code against a local model. You want the model that actually tops SWE-rebench, not the one your IDE plugin ships with by default. You're tired of paying Anthropic per task while your agent loops fifty times to fix one Django migration. Qwen3-Coder-Next is the obvious answer — but only if your GPU is sized for an 80B MoE with a 256K context window, not for an 8B dense model running autocomplete.

If you're earlier in the funnel and still picking which coding model, start with my broader coding LLM GPU guide first. This piece assumes Qwen3-Coder-Next is already locked in.

Why the 80B MoE / 3B active math matters

Qwen3-Coder-Next is a Mixture-of-Experts model: 80B total parameters, but only ~3B route active per token. The Q4 quantized weights compress to roughly 24GB, which is exactly the breakpoint where consumer cards become viable. Compare that to a dense 70B coder model — those still want 48GB minimum at Q4 — and the picture clears up fast. A single 4090 handles Qwen3-Coder. A single 4090 does not handle a dense 70B.

This is the same architectural trick the broader Qwen 3 family leans on, but Qwen3-Coder-Next pushes it further with the 256K context window for whole-repo reasoning.

Qwen3-Coder VRAM requirements

VRAM chart available at the original article

A few sharp edges in that table. Q4 weights barely fit on a 24GB card at 8K context — push to 128K and you're 10GB over. The honest workflow on a 4090 is Q4 weights, 32K-64K effective context, and aggressive context compression in your agent framework. If you genuinely need the full 256K for whole-repo work, you're looking at dual 24GB GPUs, a 48GB workstation card, or cloud. For the underlying math on why KV cache balloons like this, see my VRAM sizing guide.

FP16 at 160GB is the multi-GPU / cloud lane and has no business on a consumer rig. Skip it.

Best GPUs for Qwen3-Coder ranked

See the recommended pick on the original guide

The split is brutal but honest. The 24GB+ tier runs Qwen3-Coder-Next at Q4 the way it's meant to run. The 16GB tier runs Q2 — which works for autocomplete but falls apart the moment your agent tries to chain three tool calls. The dual 3090 setup is the dark-horse pick: $1,400 gets you 48GB combined, which is the only sub-$2,000 path to running Q8 (the quant where Qwen3-Coder's SWE-rebench numbers actually reproduce).

Don't bother running Qwen3-Coder locally if you only need autocomplete

A contrarian aside, because this matters. If your entire use case is in-editor autocomplete — Tab-to-complete, single-line suggestions, occasional 20-line fills — you do not need an 80B MoE coding model. Codestral 22B fits on a 12GB card, runs at 60+ tok/s, and produces autocomplete output that is indistinguishable from Qwen3-Coder for that task. The 80B / 3B-active architecture is overkill for next-token prediction on familiar syntax.

Qwen3-Coder-Next earns its keep when you're running agentic loops: Cline planning a refactor across 12 files, Aider editing a Django app with whole-repo grep context, Continue.dev's agent mode chaining tool calls. That's where the SWE-rebench ranking actually shows up in your productivity. If you're not doing that, save $1,000 and buy a 4070 Ti Super for Codestral.

Which GPU should YOU buy?

• Single-agent coding loop (Cline / Aider / Continue.dev agent mode): RTX 4090 24GB. Q4 at 32K-64K context covers 90% of real coding sessions. Pair it with Ollama or vLLM for the cleanest local serving stack.
• Multi-agent orchestration (Roo Code with parallel agents, LangGraph swarms): RTX 5090 32GB at $2,000. Parallel agents share KV budget — you need the headroom, and the 30-40% tok/s uplift compounds across loop iterations.
• RAG-heavy whole-repo workflows: Dual RTX 3090 at ~$1,400 for 48GB combined, or step up to a 5090. The 256K context window only matters if you actually pipe whole repos through it — most coding agents do not. But if yours does (codebase migration tools, large-scale refactors, security audits), you need the VRAM.
• Batch / overnight evaluator runs: Used RTX 3090 for ~$700, or skip local and burst to cloud H100s. Buying a $2,000 card for a workload that runs four hours a night is not financially sane.

For full 256K context, FP8 production inference, or any fine-tuning on top of Qwen3-Coder-Next, RunPod's H100 / B200 instances are the path of least resistance. The math flips at around 6-8 hours of daily use — below that, rent; above that, buy.

Common Qwen3-Coder mistakes I see constantly

• Buying 24GB and trying to run FP16. It will not fit. Q4 is the practical floor on a 4090, full stop. The marketing copy that lists Qwen3-Coder-Next as "80B" trips people into thinking they need 160GB — they don't, but they also can't run the unquantized weights on consumer hardware.
• Underestimating 256K context KV cache. Cline and Roo Code will happily fill the full window if you let them, and a 256K context can cost 18GB of cache on top of weights. Cap your context in the agent config to 32K-64K unless you specifically need whole-repo reasoning.
• Running Q2 in an agent loop because "it works in chat." Q2 Qwen3-Coder produces serviceable single-shot code. It also produces broken JSON tool calls roughly every 30 turns. By the time you've debugged your first wedged Cline session at 2am, the $400 you saved on a 16GB card is gone. This is the same trap that bites people running 70B models on undersized rigs.
• Treating Qwen3-Coder like a generic agentic model. It is tuned hard for code, tool-calling, and repo-scale reasoning. If you're running it for general chat or research, you're paying VRAM tax for capability you don't use. Pick a smaller general-purpose model for those tasks.

Final verdict

See the recommended pick on the original guide

If you're running Qwen3-Coder-Next to drive real coding agents, buy the 24GB card — anything less turns your Cline loop into a coin flip.

Related guides on Best GPU for LLM

• Best GPU for Qwen Models in 2026 (Qwen 3 + 3.6 Picks)
• Best GPU for Qwen 3.6 in 2026 (35B-A3B MoE Guide)
• Best Budget GPU for Local LLM 2026: RTX 3060 to $350

Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.