What it costs to run Qwen3-Coder-Next 80B-A3B (MoE)
Trying to decide what to buy? The best GPU for Qwen3-Coder-Next 80B-A3B (MoE) →
Cheapest way to rent it right now
$0.07/hr
NVIDIA Tesla V100 32GB SXM2 · IQ3_XS · spot · ≈ $16/mo at 8h/day
Live price captured Sun, 04 Oct 2026 18:21:15 GMT. Referral link — we may earn a commission at no cost to you; it never changes which card is cheapest.
Memory needed, by quantisation
At 16k context. Weights + KV cache + ~0.8 GB overhead.
| Quant | Weights | KV cache | Total | Quality |
|---|---|---|---|---|
| F16 | 148.4 GB | 0.4 GB | 149.6 GB | Full precision. Reference quality, twice the size of Q8 for no practical gain in most chat use. |
| Q8_0 | 78.8 GB | 0.4 GB | 80.0 GB | Effectively lossless. Use when VRAM is not the constraint. |
| Q6_K | 60.8 GB | 0.4 GB | 62.0 GB | Very close to Q8 at meaningfully less memory. A safe high-quality pick. |
| Q5_K_M | 52.7 GB | 0.4 GB | 53.8 GB | Small, hard-to-notice quality loss. Good balance. |
| Q5_0 | 51.4 GB | 0.4 GB | 52.5 GB | Older-style 5-bit. Q5_K_M is usually the better pick at the same size. |
| Q4_K_M | 45.0 GB | 0.4 GB | 46.1 GB | The community default. Best quality-per-gigabyte for most people. |
| Q4_0 | 42.2 GB | 0.4 GB | 43.4 GB | Older-style 4-bit, measurably worse than Q4_K_M at a similar size. Avoid unless required. |
| IQ4_XS | 39.4 GB | 0.4 GB | 40.6 GB | Newer 4-bit, smaller than Q4_K_M with comparable quality. Needs a recent llama.cpp. |
| Q3_K_M | 36.3 GB | 0.4 GB | 37.4 GB | Noticeable degradation. Use to fit a larger model that would otherwise not run. |
| Q2_K | 31.1 GB | 0.4 GB | 32.2 GB | Heavy degradation. Almost always better to run a smaller model at Q4_K_M instead. |
| IQ3_XS | 30.6 GB | 0.4 GB | 31.8 GB | Aggressive. Usually better than a smaller model at Q4, but test before trusting it. |
Cards that can run it
Best quantisation each card fits at 16k context, with live rental price where we track one.
| GPU | VRAM | Best fit | Uses | ~tok/s | Rent from |
|---|---|---|---|---|---|
| Apple M3 Ultra | 512 GB | F16 | 150 GB | — | — |
| Apple M2 Ultra | 192 GB | F16 | 150 GB | — | — |
| Apple M4 Max (16-core CPU / 40-core GPU) | 128 GB | Q8_0 | 80 GB | — | — |
| AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB) | 128 GB | Q8_0 | 80 GB | — | — |
| NVIDIA DGX Spark (GB10 Grace Blackwell) | 128 GB | Q8_0 | 80 GB | — | — |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | 96 GB | Q8_0 | 80 GB | — | $1.690/hrRunpod |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96 GB | Q8_0 | 80 GB | — | — |
| Apple M2 Max | 96 GB | Q8_0 | 80 GB | — | — |
| Apple M4 Pro | 64 GB | Q6_Ktight | 62 GB | — | — |
| NVIDIA RTX 6000 Ada Generation | 48 GB | Q4_K_Mtight | 46 GB | — | $0.521/hrVast.ai |
| AMD Radeon PRO W7900 | 48 GB | Q4_K_Mtight | 46 GB | — | — |
| NVIDIA RTX A6000 | 48 GB | Q4_K_Mtight | 46 GB | — | $0.330/hrRunpod |
| Apple M4 Max (14-core CPU / 32-core GPU) | 36 GB | Q2_K | 32 GB | — | — |
| NVIDIA GeForce RTX 5090 | 32 GB | IQ3_XStight | 32 GB | — | $0.374/hrVast.ai |
| NVIDIA RTX 5000 Ada Generation | 32 GB | IQ3_XStight | 32 GB | — | $0.427/hrVast.ai |
How these numbers are produced
- Model shape comes from the model's own config.json — 12 layers, 16 attention heads, 2 KV heads.
- Weight size is parameters × effective bits-per-weight. Those constants are checked against real published quantised file sizes — median error 0.7% across 40 measurements.
- KV cache is 2 × layers × KV-heads × head-dim × context × 2 bytes. Using KV-heads rather than attention heads is what makes this correct for grouped-query attention; treating a GQA model as multi-head overstates the cache by up to 8×.
- Tokens/sec is an estimate, not a benchmark. Generation is memory-bandwidth-bound, so this is bandwidth ÷ weight-bytes derated to 75%. Real throughput depends on your runtime, batch size and kernels. We don't run our own hardware tests — see our editorial policy.
- This is a mixture-of-experts model. Generation reads only the experts active for each token (10 of 512), so throughput is scored on ~3.0B active parameters, not the full 80B. Memory is the opposite: every expert must still be resident in VRAM, so the figures above use the full weight set.Hybrid MoE (Qwen3-Next architecture): 48 layers, full attention every 4th (full_attention_interval 4) = 12 full-attention + 36 linear-attention layers; `layers` = the 12 KV layers (linear layers keep a small fixed state, ~0.1 GB). 512 experts, 10 routed + 1 shared per token; active = vendor-published 3B, but all ~80B weights stay resident. HF config.json read 4 Oct 2026.
- Some memory-bandwidth figures are not yet independently verified.Where that's the case we show no tokens/sec at all rather than a number we can't stand behind.
- Rental prices are pulled hourly from provider APIs (last updated Sun, 04 Oct 2026 18:21:15 GMT). Spot/interruptible pricing can change or vanish without notice.
Try it interactively
Drag the context slider and watch the KV cache fill the card
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