What it costs to run DeepSeek V4 Pro (MoE)
Trying to decide what to buy? The best GPU for DeepSeek V4 Pro (MoE) →
Memory needed, by quantisation
At 16k context. Weights + KV cache + ~0.8 GB overhead.
| Quant | Weights | KV cache | Total | Quality |
|---|---|---|---|---|
| F16 | 2978.1 GB | 0.1 GB | 2978.9 GB | Full precision. Reference quality, twice the size of Q8 for no practical gain in most chat use. |
| Q8_0 | 1582.1 GB | 0.1 GB | 1583.0 GB | Effectively lossless. Use when VRAM is not the constraint. |
| Q6_K | 1221.0 GB | 0.1 GB | 1221.9 GB | Very close to Q8 at meaningfully less memory. A safe high-quality pick. |
| Q5_K_M | 1057.2 GB | 0.1 GB | 1058.1 GB | Small, hard-to-notice quality loss. Good balance. |
| Q5_0 | 1031.2 GB | 0.1 GB | 1032.0 GB | Older-style 5-bit. Q5_K_M is usually the better pick at the same size. |
| Q4_K_M | 902.7 GB | 0.1 GB | 903.6 GB | The community default. Best quality-per-gigabyte for most people. |
| Q4_0 | 846.9 GB | 0.1 GB | 847.7 GB | Older-style 4-bit, measurably worse than Q4_K_M at a similar size. Avoid unless required. |
| IQ4_XS | 791.1 GB | 0.1 GB | 791.9 GB | Newer 4-bit, smaller than Q4_K_M with comparable quality. Needs a recent llama.cpp. |
| Q3_K_M | 727.8 GB | 0.1 GB | 728.6 GB | Noticeable degradation. Use to fit a larger model that would otherwise not run. |
| Q2_K | 623.5 GB | 0.1 GB | 624.4 GB | Heavy degradation. Almost always better to run a smaller model at Q4_K_M instead. |
| IQ3_XS | 614.2 GB | 0.1 GB | 615.1 GB | Aggressive. Usually better than a smaller model at Q4, but test before trusting it. |
How these numbers are produced
- Model shape comes from the model's own config.json — 61 layers, 128 attention heads, 1 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 (6 of 384), so throughput is scored on ~49.0B active parameters, not the full 1599B. Memory is the opposite: every expert must still be resident in VRAM, so the figures above use the full weight set.Compressed attention (CSA + HCA, model card): each layer caches one shared 512-dim KV entry per compressed block, 30 layers at 1/4 of tokens (CSA) and 31 at 1/128 (HCA) per compress_ratios in config.json, plus a fixed 128-token sliding window and CSA indexer keys (128-dim at 1/4). That is ~4,900 cached numbers per token, stored mostly as FP8 in DeepSeek's implementation (the same math for V4-Flash matches the ~3.6 KB/token implied by the V4.1-Flash card's "1/4 of V4-Flash" = 890 B), so ~5.1 KB per token. Mapped so the engine's fp16 formula (2 x layers x kv_heads x head_dim x 2 B) gives that: 61 layers x 1 KV head x head_dim 21 = 5,124 B/token (~5 GB at 1M context). head_dim 21 is a mapping, not the model's 512; a q8/q4 KV setting in the calculator would understate it. Vendor: 1.6T total / 49B active; params = HF safetensors total; experts FP4, rest FP8. HF config.json + model card read 29 Oct 2026.
- Rental prices are pulled hourly from provider APIs (last updated Thu, 29 Oct 2026 04:20:06 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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