What it costs to run MiniMax-M3 (MoE)
Trying to decide what to buy? The best GPU for MiniMax-M3 (MoE) →
Cheapest way to rent it right now
$2.39/hr
AMD Instinct MI300X · Q2_K · on-demand · ≈ $574/mo at 8h/day
Live price captured Thu, 29 Oct 2026 04:20:06 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 | 795.4 GB | 1.9 GB | 798.1 GB | Full precision. Reference quality, twice the size of Q8 for no practical gain in most chat use. |
| Q8_0 | 422.6 GB | 1.9 GB | 425.2 GB | Effectively lossless. Use when VRAM is not the constraint. |
| Q6_K | 326.1 GB | 1.9 GB | 328.8 GB | Very close to Q8 at meaningfully less memory. A safe high-quality pick. |
| Q5_K_M | 282.4 GB | 1.9 GB | 285.0 GB | Small, hard-to-notice quality loss. Good balance. |
| Q5_0 | 275.4 GB | 1.9 GB | 278.1 GB | Older-style 5-bit. Q5_K_M is usually the better pick at the same size. |
| Q4_K_M | 241.1 GB | 1.9 GB | 243.8 GB | The community default. Best quality-per-gigabyte for most people. |
| Q4_0 | 226.2 GB | 1.9 GB | 228.9 GB | Older-style 4-bit, measurably worse than Q4_K_M at a similar size. Avoid unless required. |
| IQ4_XS | 211.3 GB | 1.9 GB | 213.9 GB | Newer 4-bit, smaller than Q4_K_M with comparable quality. Needs a recent llama.cpp. |
| Q3_K_M | 194.4 GB | 1.9 GB | 197.0 GB | Noticeable degradation. Use to fit a larger model that would otherwise not run. |
| Q2_K | 166.5 GB | 1.9 GB | 169.2 GB | Heavy degradation. Almost always better to run a smaller model at Q4_K_M instead. |
| IQ3_XS | 164.1 GB | 1.9 GB | 166.7 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 | Q8_0 | 425 GB | — | — |
| Apple M2 Ultra | 192 GB | Q2_K | 169 GB | — | — |
How these numbers are produced
- Model shape comes from the model's own config.json — 60 layers, 64 attention heads, 4 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 (4 of 128), so throughput is scored on ~23.0B active parameters, not the full 427B. Memory is the opposite: every expert must still be resident in VRAM, so the figures above use the full weight set.MoE: first 3 layers dense, 57 MoE layers with 128 routed experts (top-4) + 1 shared. All 60 layers are full attention with GQA (4 KV heads x 128); block-sparse attention picks which blocks to read but still caches the full KV, so all 60 layers count. The 57 sparse layers also keep indexer keys (4 index heads x 128) that are NOT counted here; if cached per token they add up to ~47% to the KV figure. Active ~23B and total ~428B are vendor figures (model card); params = HF safetensors total, native BF16 (~854 GB), includes the vision encoder. Release date = first commit on HF (12 Jun 2026). HF config.json + model card read 29 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 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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