TinyLlama 1.1B Chat memory requirements

1.1B parameters — 2.0 GiB of weights at FP16, 633 MiB at Q4_K_M, and 22.0 KiB of KV cache per token on top. Solved against 22 real accelerators; 22 of them can hold it.

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Parameters

1.1B

1,100,048,384 exactly

Weights, Q4_K_M

633 MiB

2.0 GiB unquantized

KV cache

22.0 KiB

per token · 22L × 4 KV × 64

Smallest card (Q4)

8 GB

Jetson Orin Nano Super (8GB)

Weights at each precision

Precision	Bits/weight	Weights	Source	Notes
FP16 / BF16	16.00	2.0 GiB	measured file	Unquantized. Two bytes per parameter, exactly.
Q8_0	8.50	1.1 GiB	nominal	Effectively lossless; the usual reference point for quantized quality.
Q6_K	6.56	860 MiB	nominal	Quality loss is hard to measure on most benchmarks.
Q5_K_M	5.67	744 MiB	nominal	A middle point when Q4 fits with too little room for context.
Q4_K_M	4.83	633 MiB	nominal	The default choice for local inference — the best size/quality knee.
Q3_K_M	3.91	513 MiB	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	2,048	365 tok/s
H200 141GB (SXM)	141 GB	4800 GB/s	FP16 / BF16	2,048	2137 tok/s
Apple M4 Max (128GB)	128 GB	546 GB/s	FP16 / BF16	2,048	243 tok/s
H100 80GB (SXM5)	80 GB	3350 GB/s	FP16 / BF16	2,048	1491 tok/s
A100 80GB (SXM)	80 GB	2039 GB/s	FP16 / BF16	2,048	908 tok/s
Jetson AGX Orin (64GB)	64 GB	204.8 GB/s	FP16 / BF16	2,048	91 tok/s
RTX 6000 Ada Generation	48 GB	960 GB/s	FP16 / BF16	2,048	427 tok/s
L40S	48 GB	864 GB/s	FP16 / BF16	2,048	385 tok/s
Apple M4 Pro (48GB)	48 GB	273 GB/s	FP16 / BF16	2,048	122 tok/s
A100 40GB (SXM)	40 GB	1555 GB/s	FP16 / BF16	2,048	692 tok/s
GeForce RTX 5090	32 GB	1792 GB/s	FP16 / BF16	2,048	798 tok/s
GeForce RTX 4090	24 GB	1008 GB/s	FP16 / BF16	2,048	449 tok/s
Radeon RX 7900 XTX	24 GB	960 GB/s	FP16 / BF16	2,048	427 tok/s
GeForce RTX 3090	24 GB	936 GB/s	FP16 / BF16	2,048	417 tok/s
Apple M4 (24GB)	24 GB	120 GB/s	FP16 / BF16	2,048	53 tok/s
GeForce RTX 5080	16 GB	960 GB/s	FP16 / BF16	2,048	427 tok/s
GeForce RTX 5070 Ti	16 GB	896 GB/s	FP16 / BF16	2,048	399 tok/s
GeForce RTX 4080 SUPER	16 GB	736 GB/s	FP16 / BF16	2,048	328 tok/s
GeForce RTX 4070 Ti SUPER	16 GB	672 GB/s	FP16 / BF16	2,048	299 tok/s
GeForce RTX 4060 Ti 16GB	16 GB	288 GB/s	FP16 / BF16	2,048	128 tok/s
GeForce RTX 3060 12GB	12 GB	360 GB/s	FP16 / BF16	2,048	160 tok/s
Jetson Orin Nano Super (8GB)	8 GB	102 GB/s	FP16 / BF16	2,048	45 tok/s

Questions about this model

How much VRAM does TinyLlama 1.1B Chat need?

2.0 GiB for the weights at FP16 and 633 MiB at Q4_K_M, from an exact count of 1,100,048,384 parameters. On top of that, the KV cache costs 22.0 KiB per token of context — 176 MiB at 8k and 704 MiB at 32k.

What is the smallest GPU that runs TinyLlama 1.1B Chat?

Of the 22 accelerators here, the smallest that holds Q4_K_M weights is the Jetson Orin Nano Super (8GB) at 8 GB — 633 MiB of weights against 6.0 GiB usable, leaving room for 2k tokens. For unquantized weights you need at least a Jetson Orin Nano Super (8GB).

Why is the KV cache for TinyLlama 1.1B Chat the size it is?

Because it stores one key and one value vector per token, per layer: 22 layers × 4 KV heads × 64 dimensions × 2 (K and V) × 2 bytes = 22.0 KiB per token. Grouped-query attention shares those 4 KV heads across 32 query heads, cutting the cache by 8× 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.