Qwythos 9B Claude Mythos 5 1M memory requirements

9.4B parameters — 17.5 GiB of weights at FP16, 5.3 GiB at Q4_K_M, and 128.0 KiB of KV cache per token on top. Solved against 22 real accelerators; 22 of them can hold it.

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Parameters

9.4B

9,409,813,744 exactly

Weights, Q4_K_M

5.3 GiB

17.5 GiB unquantized

KV cache

128.0 KiB

per token · 32L × 4 KV × 256

Smallest card (Q4)

8 GB

Jetson Orin Nano Super (8GB)

Weights at each precision

Precision	Bits/weight	Weights	Source	Notes
FP16 / BF16	16.00	17.5 GiB	measured file	Unquantized. Two bytes per parameter, exactly.
Q8_0	8.50	9.3 GiB	nominal	Effectively lossless; the usual reference point for quantized quality.
Q6_K	6.56	7.2 GiB	nominal	Quality loss is hard to measure on most benchmarks.
Q5_K_M	5.67	6.2 GiB	nominal	A middle point when Q4 fits with too little room for context.
Q4_K_M	4.83	5.3 GiB	nominal	The default choice for local inference — the best size/quality knee.
Q3_K_M	3.91	4.3 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	1,048,576	41 tok/s
H200 141GB (SXM)	141 GB	4800 GB/s	FP16 / BF16	919,083	241 tok/s
Apple M4 Max (128GB)	128 GB	546 GB/s	FP16 / BF16	642,848	27 tok/s
H100 80GB (SXM5)	80 GB	3350 GB/s	FP16 / BF16	459,348	168 tok/s
A100 80GB (SXM)	80 GB	2039 GB/s	FP16 / BF16	459,348	102 tok/s
Jetson AGX Orin (64GB)	64 GB	204.8 GB/s	FP16 / BF16	249,632	10 tok/s
RTX 6000 Ada Generation	48 GB	960 GB/s	FP16 / BF16	218,175	48 tok/s
L40S	48 GB	864 GB/s	FP16 / BF16	218,175	43 tok/s
Apple M4 Pro (48GB)	48 GB	273 GB/s	FP16 / BF16	151,328	14 tok/s
A100 40GB (SXM)	40 GB	1555 GB/s	FP16 / BF16	157,882	78 tok/s
GeForce RTX 5090	32 GB	1792 GB/s	FP16 / BF16	97,589	90 tok/s
GeForce RTX 4090	24 GB	1008 GB/s	FP16 / BF16	37,296	51 tok/s
Radeon RX 7900 XTX	24 GB	960 GB/s	FP16 / BF16	37,296	48 tok/s
GeForce RTX 3090	24 GB	936 GB/s	FP16 / BF16	37,296	47 tok/s
Apple M4 (24GB)	24 GB	120 GB/s	Q8_0	71,177	11 tok/s
GeForce RTX 5080	16 GB	960 GB/s	Q8_0	44,308	87 tok/s
GeForce RTX 5070 Ti	16 GB	896 GB/s	Q8_0	44,308	81 tok/s
GeForce RTX 4080 SUPER	16 GB	736 GB/s	Q8_0	44,308	66 tok/s
GeForce RTX 4070 Ti SUPER	16 GB	672 GB/s	Q8_0	44,308	61 tok/s
GeForce RTX 4060 Ti 16GB	16 GB	288 GB/s	Q8_0	44,308	26 tok/s
GeForce RTX 3060 12GB	12 GB	360 GB/s	Q8_0	14,161	33 tok/s
Jetson Orin Nano Super (8GB)	8 GB	102 GB/s	Q4_K_M	5,808	16 tok/s

Questions about this model

How much VRAM does Qwythos 9B Claude Mythos 5 1M need?

17.5 GiB for the weights at FP16 and 5.3 GiB at Q4_K_M, from an exact count of 9,409,813,744 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 Qwythos 9B Claude Mythos 5 1M?

Of the 22 accelerators here, the smallest that holds Q4_K_M weights is the Jetson Orin Nano Super (8GB) at 8 GB — 5.3 GiB of weights against 6.0 GiB usable, leaving room for 6k tokens. For unquantized weights you need at least a GeForce RTX 4090.

Why is the KV cache for Qwythos 9B Claude Mythos 5 1M the size it is?

Because it stores one key and one value vector per token, per layer: 32 layers × 4 KV heads × 256 dimensions × 2 (K and V) × 2 bytes = 128.0 KiB per token. Grouped-query attention shares those 4 KV heads across 16 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.