If you’re building a local coding agent stack on top of Qwen3-Coder-Next, the first thing to settle is whether it fits at all. The “80B total, 3B active” framing makes the model sound like it slots onto a mid-range card. It does not. The published Q4_K_M download is 48.5GB, and MoE weights all have to be resident — only the compute is sparse, not the memory.
Quick answer: there is no single consumer card for this model. The cheapest setup that runs it properly is two used RTX 3090s, about $1,640 for 48GB combined, at Q3_K_M with a 128K context. A single 32GB RTX 5090 manages Q2_K at 32K and nothing more. A 24GB card does not hold any published quantization of Qwen3-Coder-Next.
NVIDIA GeForce RTX 3090
24GB GDDR6XTwo of these give 48GB for about $1,640, which holds Qwen3-Coder-Next at Q3_K_M with a 128K coding context. One card holds none of its quantizations.
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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 per token for an agent that burns thousands of them on a single refactor.
If you’re earlier in the funnel and still picking which coding model, start with the broader coding LLM GPU guide first. This piece assumes Qwen3-Coder-Next is already locked in.
Why “3B active” does not shrink the VRAM bill
Qwen3-Coder-Next is a Mixture-of-Experts model: 80B total parameters across 512 experts, with 10 routed per token, which works out to roughly 3B active. That sparsity buys speed, because each token only touches a fraction of the weights. It buys nothing on capacity, because the router can select any expert at any token, so all 80B have to sit in memory.
This is the single most expensive misreading of MoE models, and it is easy to make: a dense 80B and a sparse 80B cost the same to hold and very different amounts to run.
This is the same architectural trick the broader Qwen 3 family leans on, but Qwen3-Coder-Next pushes it further with a 256K context window for whole-repo reasoning.
Qwen3-Coder VRAM requirements
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
Weight sizes below are the published GGUF downloads, not estimates from the parameter count. The KV cache column comes from the model’s own config: 48 layers, but only every fourth uses full attention, so 12 attention layers carry cache, at 2 key/value heads of 256 dimensions each.
| Quant | Published size | KV @ 32K | KV @ 128K | KV @ 256K | Total @ 128K |
|---|---|---|---|---|---|
| Q2_K | 29.2GB | 0.75GB | 3GB | 6GB | ~34GB |
| Q3_K_S | 34.6GB | 0.75GB | 3GB | 6GB | ~39GB |
| Q3_K_M | 38.3GB | 0.75GB | 3GB | 6GB | ~43GB |
| Q4_K_M | 48.5GB | 0.75GB | 3GB | 6GB | ~53GB |
| BF16 | ~160GB | 1.5GB | 6GB | 12GB | ~168GB |
Two things in that table are worth sitting with.
The KV cache is tiny for a 256K model — 6GB at the full window, where a dense 70B would want 80GB for the same length. That is the hybrid attention design paying off: three quarters of the layers use linear attention, whose state does not grow with context. Long context is genuinely cheap here.
The weights are the entire problem. Total VRAM barely moves as you extend context, so there is no configuration where a smaller window rescues a card that cannot hold the model. Either the quantization fits or it does not.
Best GPUs for Qwen3-Coder ranked
| Setup | VRAM | Best quant that fits | Context at that quant | Price |
|---|---|---|---|---|
| 4x RTX 3090 (used) | 96GB | Q4_K_M | 256K, comfortably | ~$3,280 |
| 2x RTX 5090 | 64GB | Q4_K_M | 256K | ~$9,800 |
| 2x RTX 3090 (used) | 48GB | Q3_K_M | 128K | ~$1,640 |
| NVIDIA A6000 | 48GB | Q3_K_M | 128K | ~$3,500 |
| RTX 5090 | 32GB | Q2_K | 32K, tight | ~$4,900 |
| RTX 4090 / 3090 | 24GB | none | — | ~$2,200 / ~$820 |
| 16GB cards | 16GB | none | — | — |
The split is not the usual one. Normally 24GB is the line between “runs it” and “runs it badly”; here 24GB is on the wrong side of “runs it at all”, and the interesting decision is between 48GB at Q3 and 96GB at Q4. Two used 3090s beat a single 5090 for this model outright — more memory, less than a fifth of the price — and the 5090’s advantage in bandwidth cannot be spent on a model it can only hold at Q2.
Don’t run Qwen3-Coder locally if you only need autocomplete
A contrarian aside, because it matters more now that the entry price is a two-card build. 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 comfortably on one 24GB card and answers faster.
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 is where the SWE-rebench gap shows up, and where the 256K window and its unusually cheap cache actually get used.
Which GPU should you buy?
- The honest answer is two of them. Cheapest setup that genuinely runs it: two used RTX 3090s, roughly $1,640 for 48GB. Q3_K_M at 128K covers whole-repo agent work. Layer-split with llama.cpp or run tensor-parallel under vLLM.
- Q4 quality at the full window: four used RTX 3090s for 96GB at about $3,280, or two RTX 5090s for 64GB at $9,800. The 3090 build is less than half the price for more memory; the 5090 build is quieter, cooler and far simpler to power.
- One card, single slot, no compromises on noise: an A6000 at ~$3,500 gives the same 48GB as two 3090s in one 300W slot. You are paying roughly $1,900 for that convenience.
- Occasional use: don’t buy. A multi-card build for a workload that runs a few hours a night is not financially sane.
For Q4 at the full 256K window, FP8 production inference, or any fine-tuning on top of Qwen3-Coder-Next, RunPod’s H100 and B200 instances are the path of least resistance. With the local entry point now a $1,640 two-card build rather than a single $2,200 card, the rent-versus-buy line has moved: local wins sooner on hardware cost, and later on setup effort.
Common mistakes with Qwen3-Coder
- Reading “3B active” as “3B of VRAM”. Sparsity is a compute property. Every one of the 512 experts has to be resident because the router can reach any of them on any token.
- Assuming a smaller context window rescues a card. It does not, and this model is the clearest example: dropping from 256K to 32K saves about 5GB, against weights of 48.5GB. If the quantization does not fit, nothing about your agent config will make it fit.
- Running Q2 in an agent loop because “it works in chat”. Q2_K produces serviceable single-shot code. It also produces broken JSON tool calls often enough to wedge a Cline session. Q3_K_M on 48GB is the sensible floor for tool-calling work.
- Treating Qwen3-Coder like a generic agentic model. It is tuned hard for code, tool-calling, and repo-scale reasoning. Running it for general chat spends a lot of VRAM on capabilities you are not using.
Final verdict
| Need | Best pick | Price |
|---|---|---|
| Cheapest setup that runs it | 2x RTX 3090 (48GB) | ~$1,640 |
| Q4 at the full 256K window | 4x RTX 3090 (96GB) | ~$3,280 |
| Same 48GB in one slot | NVIDIA A6000 | ~$3,500 |
| Single-card, Q2 only | RTX 5090 (32GB) | ~$4,900 |
| Burst / fine-tuning | RunPod H100 | hourly |
NVIDIA GeForce RTX 3090
24GB GDDR6X48GB for about $1,640 across two used cards, holding Q3_K_M at a 128K context. The cheapest configuration that runs Qwen3-Coder-Next for Continue.dev, Cline and Aider.
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Frequently asked questions
How much VRAM do I need for Qwen3-Coder-Next locally?
At least 48GB. The published Q4_K_M download is 48.5GB and Q3_K_M is 38.3GB, so a 48GB setup — two used RTX 3090s or an A6000 — is the first tier that holds a quantization worth using for agent work. The KV cache adds very little: 3GB at 128K context, because only 12 of the model’s 48 layers use full attention.
Can the RTX 4090 run Qwen3-Coder-Next?
No. The smallest published quantization, Q2_K, is 29.2GB, which is already past a 4090’s 24GB before any cache or runtime overhead. This is unusual — a 4090 handles most 70B-class models at Q4 — and it catches people out because the model is advertised as having only 3B active parameters. Active parameters set speed, not capacity.
Is Qwen3-Coder-Next worth running locally vs Anthropic or OpenAI APIs?
It depends on call volume, and the bar is higher than it used to be. Heavy agentic loops — Cline, Aider or Continue.dev agent mode firing dozens to hundreds of calls per task — are where local wins, but the entry cost is now a two-card 48GB build rather than a single GPU. Below a few hours of daily use, renting is still the better arithmetic.
What’s the difference between Qwen3-Coder-Next and a dense 70B coder model?
Speed, not size. Qwen3-Coder-Next is 80B total with roughly 3B active per token, so it generates far faster than its parameter count suggests, but all 80B stay resident: 48.5GB at Q4_K_M against roughly 43GB for a dense 70B at the same quantization. Where it genuinely wins is long context — its hybrid attention keeps the cache at about 6GB even at 256K.
Can I run Qwen3-Coder on a 16GB GPU like the RTX 4060 Ti or 5070 Ti?
No. The smallest published build is 29.2GB, so a 16GB card is short by more than the card itself holds, and no context setting changes that. For coding work on 16GB, a smaller dedicated coder model is the realistic path.
Qwen3-Coder-Next is the model that breaks the “24GB is enough” habit. Budget for 48GB, and two used 3090s are the cheapest way there.