Mixtral 8x7B Instruct memory requirements

46.7B parameters — 87.0 GiB of weights at FP16, 26.3 GiB at Q4_K_M, and 128.0 KiB of KV cache per token on top. Solved against 22 real accelerators; 14 of them can hold it.

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Parameters

46.7B

46,702,792,704 exactly

Weights, Q4_K_M

26.3 GiB

87.0 GiB unquantized

KV cache

128.0 KiB

per token · 32L × 8 KV × 128

Smallest card (Q4)

32 GB

GeForce RTX 5090

Weights at each precision

Precision	Bits/weight	Weights	Source	Notes
FP16 / BF16	16.00	87.0 GiB	measured file	Unquantized. Two bytes per parameter, exactly.
Q8_0	8.50	46.2 GiB	nominal	Effectively lossless; the usual reference point for quantized quality.
Q6_K	6.56	35.7 GiB	nominal	Quality loss is hard to measure on most benchmarks.
Q5_K_M	5.67	30.8 GiB	nominal	A middle point when Q4 fits with too little room for context.
Q4_K_M	4.83	26.3 GiB	nominal	The default choice for local inference — the best size/quality knee.
Q3_K_M	3.91	21.3 GiB	nominal	Measurable quality loss. Worth it only to make a model fit at all.

Every accelerator

Largest quant that fits with at least 4k of context, the context it leaves, and the bandwidth ceiling on decode speed. Each row links to the worked page for that pairing.

Accelerator	Memory	Bandwidth	Best quant	Max context	Ceiling
Apple M3 Ultra (512GB)	512 GB	819 GB/s	FP16 / BF16	32,768	31 tok/s
H200 141GB (SXM)	141 GB	4800 GB/s	FP16 / BF16	32,768	179 tok/s
Apple M4 Max (128GB)	128 GB	546 GB/s	FP16 / BF16	32,768	20 tok/s
H100 80GB (SXM5)	80 GB	3350 GB/s	Q8_0	32,768	227 tok/s
A100 80GB (SXM)	80 GB	2039 GB/s	Q8_0	32,768	138 tok/s
Jetson AGX Orin (64GB)	64 GB	204.8 GB/s	Q8_0	14,632	14 tok/s
RTX 6000 Ada Generation	48 GB	960 GB/s	Q6_K	32,768	83 tok/s
L40S	48 GB	864 GB/s	Q6_K	32,768	74 tok/s
Apple M4 Pro (48GB)	48 GB	273 GB/s	Q5_K_M	32,768	27 tok/s
A100 40GB (SXM)	40 GB	1555 GB/s	Q6_K	9,288	134 tok/s
GeForce RTX 5090	32 GB	1792 GB/s	Q4_K_M	26,047	202 tok/s
GeForce RTX 4090	24 GB	1008 GB/s	Q3_K_M	6,730	140 tok/s
Radeon RX 7900 XTX	24 GB	960 GB/s	Q3_K_M	6,730	134 tok/s
GeForce RTX 3090	24 GB	936 GB/s	Q3_K_M	6,730	130 tok/s
Apple M4 (24GB)	24 GB	120 GB/s	no fit	—	—
GeForce RTX 5080	16 GB	960 GB/s	no fit	—	—
GeForce RTX 5070 Ti	16 GB	896 GB/s	no fit	—	—
GeForce RTX 4080 SUPER	16 GB	736 GB/s	no fit	—	—
GeForce RTX 4070 Ti SUPER	16 GB	672 GB/s	no fit	—	—
GeForce RTX 4060 Ti 16GB	16 GB	288 GB/s	no fit	—	—
GeForce RTX 3060 12GB	12 GB	360 GB/s	no fit	—	—
Jetson Orin Nano Super (8GB)	8 GB	102 GB/s	no fit	—	—

Questions about this model

How much VRAM does Mixtral 8x7B Instruct need?

87.0 GiB for the weights at FP16 and 26.3 GiB at Q4_K_M, from an exact count of 46,702,792,704 parameters. On top of that, the KV cache costs 128.0 KiB per token of context — 1.0 GiB at 8k and 4.0 GiB at 32k.

What is the smallest GPU that runs Mixtral 8x7B Instruct?

Of the 22 accelerators here, the smallest that holds Q4_K_M weights is the GeForce RTX 5090 at 32 GB — 26.3 GiB of weights against 29.4 GiB usable, leaving room for 25k tokens. For unquantized weights you need at least an Apple M4 Max (128GB).

Why is the KV cache for Mixtral 8x7B Instruct the size it is?

Because it stores one key and one value vector per token, per layer: 32 layers × 8 KV heads × 128 dimensions × 2 (K and V) × 2 bytes = 128.0 KiB per token. Grouped-query attention shares those 8 KV heads across 32 query heads, cutting the cache by 4× against multi-head attention. None of this can be read off the parameter count.

Compare with

Method and limits. The parameter count and every architecture figure on this page are read from the model's own published config and the Hub's index over its tensor shapes, fetched 2026-08-07 — nothing here is recalled from a model's name. Weight bytes are the size of a real published file wherever one exists and the parameter count times the published llama.cpp bits-per-weight where it does not; every row says which. Speed figures are roofline bounds — memory bandwidth divided by bytes read per token — not benchmarks. Nothing on this page is written by a language model.