What it costs to run Qwen3 32B
Trying to decide what to buy? The best GPU for Qwen3 32B →
Cheapest way to rent it right now
$0.06/hr
NVIDIA Tesla V100 32GB SXM2 · Q6_K · spot · ≈ $14/mo at 8h/day
Live price captured Sat, 10 Oct 2026 12:21:12 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 | 61.0 GB | 4.0 GB | 65.8 GB | Full precision. Reference quality, twice the size of Q8 for no practical gain in most chat use. |
| Q8_0 | 32.4 GB | 4.0 GB | 37.2 GB | Effectively lossless. Use when VRAM is not the constraint. |
| Q6_K | 25.0 GB | 4.0 GB | 29.8 GB | Very close to Q8 at meaningfully less memory. A safe high-quality pick. |
| Q5_K_M | 21.7 GB | 4.0 GB | 26.4 GB | Small, hard-to-notice quality loss. Good balance. |
| Q5_0 | 21.1 GB | 4.0 GB | 25.9 GB | Older-style 5-bit. Q5_K_M is usually the better pick at the same size. |
| Q4_K_M | 18.5 GB | 4.0 GB | 23.3 GB | The community default. Best quality-per-gigabyte for most people. |
| Q4_0 | 17.4 GB | 4.0 GB | 22.1 GB | Older-style 4-bit, measurably worse than Q4_K_M at a similar size. Avoid unless required. |
| IQ4_XS | 16.2 GB | 4.0 GB | 21.0 GB | Newer 4-bit, smaller than Q4_K_M with comparable quality. Needs a recent llama.cpp. |
| Q3_K_M | 14.9 GB | 4.0 GB | 19.7 GB | Noticeable degradation. Use to fit a larger model that would otherwise not run. |
| Q2_K | 12.8 GB | 4.0 GB | 17.6 GB | Heavy degradation. Almost always better to run a smaller model at Q4_K_M instead. |
| IQ3_XS | 12.6 GB | 4.0 GB | 17.4 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 | 66 GB | — | — |
| Apple M2 Ultra | 192 GB | F16 | 66 GB | — | — |
| Apple M4 Max (16-core CPU / 40-core GPU) | 128 GB | F16 | 66 GB | — | — |
| AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB) | 128 GB | F16 | 66 GB | — | — |
| NVIDIA DGX Spark (GB10 Grace Blackwell) | 128 GB | F16 | 66 GB | — | — |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | 96 GB | F16 | 66 GB | — | $1.690/hrRunpod |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96 GB | F16 | 66 GB | — | — |
| Apple M2 Max | 96 GB | F16 | 66 GB | — | — |
| Apple M4 Pro | 64 GB | Q8_0 | 37 GB | — | — |
| NVIDIA RTX 6000 Ada Generation | 48 GB | Q8_0 | 37 GB | — | $0.494/hrVast.ai |
| AMD Radeon PRO W7900 | 48 GB | Q8_0 | 37 GB | — | — |
| NVIDIA RTX A6000 | 48 GB | Q8_0 | 37 GB | — | $0.322/hrVast.ai |
| Apple M4 Max (14-core CPU / 32-core GPU) | 36 GB | Q6_K | 30 GB | — | — |
| NVIDIA GeForce RTX 5090 | 32 GB | Q6_Ktight | 30 GB | — | $0.402/hrVast.ai |
| NVIDIA RTX 5000 Ada Generation | 32 GB | Q6_Ktight | 30 GB | — | $0.334/hrVast.ai |
| NVIDIA GeForce RTX 4090 | 24 GB | Q4_K_Mtight | 23 GB | — | $0.323/hrVast.ai |
| AMD Radeon RX 7900 XTX | 24 GB | Q4_K_Mtight | 23 GB | — | — |
| NVIDIA GeForce RTX 3090 Ti | 24 GB | Q4_K_Mtight | 23 GB | — | $0.120/hrVast.ai |
| NVIDIA RTX A5000 | 24 GB | Q4_K_Mtight | 23 GB | — | $0.160/hrRunpod |
| NVIDIA GeForce RTX 3090 | 24 GB | Q4_K_Mtight | 23 GB | — | — |
| AMD Radeon RX 7900 XT | 20 GB | Q3_K_Mtight | 20 GB | — | — |
How these numbers are produced
- Model shape comes from the model's own config.json — 64 layers, 64 attention heads, 8 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.
- 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 Sat, 10 Oct 2026 12:21:12 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
Worth reading before you buy
Cheapest RTX 4090 rental
Live per-hour 4090 prices on Vast and Runpod, the spot-vs-on-demand lever, and what a 24 GB card actually runs — the rent-a-card answer for everything in the 14B–32B class.
RTX 5090 vs two RTX 3090s
Two 3090s give you 48 GB but not double the speed for chat — llama.cpp's default multi-GPU mode makes the cards take turns. What the benchmarks actually show.
Cheapest way to serve Llama 70B
"Cheapest" flips on duty cycle and concurrency — and in Europe the electricity bill alone can approach the cost of just renting. Plus the config defaults that silently break a 16k deployment.
GLM-4.5-Air on local hardware
The consumer-runnable GLM: VRAM by quant, which cards and unified-memory boxes clear it, and what to expect from published numbers — the written companion to this page.