What it costs to run MiniMax-M3.1-Flash (MoE)
Trying to decide what to buy? The best GPU for MiniMax-M3.1-Flash (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 Sat, 07 Nov 2026 02:20:13 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 use MiniMax Sparse Attention (block-sparse, GQA 4 KV heads x 128), which picks blocks to read but caches every token, so all 60 count; the indexer adds one 128-dim key per token per layer (+12.5%, not counted). params = 427.05B counted from tensor shapes, NOT the HF API figure (220.5B), which counts the NVFP4-packed experts (2 weights per byte) once and skips scale tensors; the official QAT checkpoint is ~250 GB (NVFP4 experts, MXFP8 attention) and excludes the 1.96B DSpark draft head. Card: ~428B total / 23B active, up to 1M context. HF config + card read Nov 7, 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 Sat, 07 Nov 2026 02:20:13 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
How much VRAM do you need?
About 0.57 GB per billion parameters at 4-bit, plus room for context: the rule behind the numbers on this page, with worked examples.
The best GPU for local AI right now
The card with enough VRAM for your model at the lowest price, budget by budget.
Rent a cloud GPU or buy one?
Rent, unless you'll pass about 2,000 hours of use: the break-even and the costs people forget on both sides.
What quantization means
Why a 4-bit copy of a model fits on a normal PC, and what you give up for it.