Computed

Mixtral 8x7B Instruct on a Jetson AGX Orin (64GB)

Yes, quantized. Q8_0 is the largest that fits, leaving room for 14k tokens of context.

Change the numbers →Mixtral 8x7B Instruct on every card Jetson AGX Orin (64GB) across every model

Verdict

Q8_0

48.0 GiB usable of 64 GB

Weights (Q4_K_M)

26.3 GiB

87.0 GiB at FP16 · 46.7B params · 1 of 6 quant rows are measured files

KV cache

128.0 KiB per token at FP16

32 layers × 8 KV heads × 128 dimensions

Speed ceiling

14 tok/s

204.8 GB/s ÷ bytes read per token

Every quant, against this card

Weight bytes are the size of the real published file wherever one exists — 1 of these6 rows are measured from the published checkpoint, the rest computed from the parameter count. Max context is what the KV cache can grow to in whatever memory the weights leave behind, capped at the 32k tokens this model was trained to address.

Bars are the weight bytes at each precision; the dashed line is the usable memory of a Jetson AGX Orin (64GB). Drawn from the computed byte counts, not sketched.

Precision	Bits/weight	Weights	Source	Fits	Max context	Ceiling
FP16 / BF16	16.00	87.0 GiB	measured file	−39.0 GiB	—	4.8 tok/s offloaded
Q8_0	8.50	46.2 GiB	nominal	yes	14,632	14 tok/s
Q6_K	6.56	35.7 GiB	nominal	yes	32,768 (model cap)	18 tok/s
Q5_K_M	5.67	30.8 GiB	nominal	yes	32,768 (model cap)	20 tok/s
Q4_K_M	4.83	26.3 GiB	nominal	yes	32,768 (model cap)	23 tok/s
Q3_K_M	3.91	21.3 GiB	nominal	yes	32,768 (model cap)	28 tok/s

What the context actually costs

The KV cache is 2 · layers · kv_heads · head_dim bytes per token per element — 2 · 32 · 8 · 128 · 2 B = 128.0 KiB. It depends on the key/value head count, not the attention-head count and not the parameter count. This model shares 8 KV heads across 32 query heads, which divides the cache by 4 against multi-head attention.

Context	KV cache (FP16)	KV cache (8-bit)	Plus Q4_K_M weights
4,096	512 MiB	256 MiB	26.8 GiB
8,192	1.0 GiB	512 MiB	27.3 GiB
32,768	4.0 GiB	2.0 GiB	30.3 GiB

The speed ceiling, and where it comes from

Generating one token reads every active weight from memory once. At Q8_0 that is 12.7 GiB — only 28% of the checkpoint, because this is a mixture of experts and each token is routed to 2 of 8, plus a pass over the KV cache. A Jetson AGX Orin (64GB) moves 204.8 GB/s, so the arithmetic ceiling is 14 tok/s. Treat it as a bound, not an estimate: attention overhead, kernel launches and imperfect memory access keep real runtimes at roughly 60–80% of it, and nothing pushes past it.

Where these numbers come from

The model

Parameters: 46,702,792,704
Layers: 32
Attention / KV heads: 32 / 8
Head dimension: 128
Trained context: 32,768
Checkpoint as published: 87.0 GiB

Read from mistralai/Mixtral-8x7B-Instruct-v0.1. The parameter count is the Hub's own total over the tensor shapes, not a figure taken from the model's name.

The accelerator

Memory: 64 GB LPDDR5 unified
Bandwidth: 204.8 GB/s
Assumed usable: 75% → 48.0 GiB

Capacity and bandwidth from the vendor's specification. The usable fraction is an assumption, not a spec: unified memory is shared with the OS and the display, and the GPU working-set cap is raisable on Apple silicon with `sudo sysctl iogpu.wired_limit_mb`.

Questions this pairing answers

How much VRAM does Mixtral 8x7B Instruct need?

87.0 GiB for the weights at FP16 — 46.7B parameters at two bytes each — and 26.3 GiB at Q4_K_M. The KV cache is on top of that and is not a fixed number: this model spends 128.0 KiB per token of context, so 8,192 tokens costs a further 1.0 GiB. A Jetson AGX Orin (64GB) makes 48.0 GiB of its 64 GB available on the assumption below.

Can a Jetson AGX Orin (64GB) run Mixtral 8x7B Instruct?

Yes, quantized. Q8_0 is the largest that fits, leaving room for 14k tokens of context. That is the weights and the KV cache together, against 48.0 GiB of usable memory.

How fast will Mixtral 8x7B Instruct run on a Jetson AGX Orin (64GB)?

No faster than 14 tokens/second at Q8_0, and in practice below it. Decoding is memory-bound: every token reads the 28% of weights this MoE routes to 12.7 GiB of weights plus the cache, and this card moves 204.8 GB/s. That division is the ceiling — no kernel, runtime or driver beats it, and a real runtime typically reaches 60–80% of it.

Why does the context length change how much memory Mixtral 8x7B Instruct needs?

Because the KV cache holds one key and one value vector per token, per layer, for the whole conversation, and it is allocated separately from the weights. This model has 32 layers and 8 key/value heads of 128 dimensions, shared across 32 query heads — grouped-query attention, which divides the cache by 4. That works out at 128.0 KiB per token. Parameter count tells you nothing about this number.

The same model on a different card

A different model on the same card

All 62 models on Jetson AGX Orin (64GB) →

Method and limits. Weight bytes are the byte size of the real published file wherever one exists, and the model's exact parameter count times the published llama.cpp bits-per-weight where it does not. That distinction is on every row above and it matters at both ends: a sub-1B model's Q4_K_M file runs a third larger than the nominal figure because k-quants keep its embedding tables at higher precision, and an already-4-bit release cannot be quantized upward at all. The KV cache is 2 · layers · kv_heads · head_dim · bytes per token, summed over layers with each sliding-window layer capped at its window. The speed figure is a roofline bound, not a benchmark: bandwidth divided by bytes read per token, which no runtime exceeds and every runtime falls short of. The usable fraction of card memory is an assumption: 75% here, which is what this lane assumes for unified memory, where the OS and window server share the same pool — the other class assumes 92%, so it is not one number applied to every device. That is the only assumed input on this page; every other figure is computed from the model config and the card's published specification. Nothing on this page is written by a language model. Architecture from the model's published config, fetched 2026-08-07.