Best GPUs for 30B local LLMs

30B-class models in Q4 fit comfortably in 24GB of VRAM with room for a useful context window — the sweet spot for a single consumer GPU. Apple Silicon Macs with 32GB+ unified memory also handle them well. Ranked from community reports.

Ranked from 486 community reports on llamaperf.

Ranked by community reports

#	GPU	VRAM	Reports	Fastest t/s
1	RTX 3090nvidia	24GB	87	382.0
2	RTX 5090nvidia	32GB	67	578.0
3	AMD Strix Halo 128GBamd	128GB	35	153.3
4	RTX Pro 6000 Blackwellnvidia	96GB	27	177.0
5	RTX 5060 Ti 16GBnvidia	16GB	26	76.7
6	DGX Sparknvidia	128GB	25	181.0
7	M5 Max 128GBapple	128GB	18	133.6
8	Radeon AI PRO R9700 32GBamd	32GB	17	280.0
9	RTX 4090nvidia	24GB	17	180.0
10	RX 7900 XTXamd	24GB	15	100.0
11	RTX 5070 Tinvidia	16GB	14	115.0
12	RTX 5080nvidia	16GB	7	75.0
13	RTX PRO 6000 Max-Qnvidia	96GB	6	240.0
14	V100 32GBnvidia	32GB	6	218.0
15	RTX 3080 20GBnvidia	20GB	6	57.5
16	M2 Max 96GBapple	96GB	6	43.0
17	RTX 4060 Ti 16GBnvidia	16GB	6	32.5
18	AMD MI50 32GBamd	32GB	6	15.5
19	H100 80GBnvidia	80GB	5	193.0
20	RTX 6000nvidia	48GB	5	150.0
21	RTX 4070 Ti Supernvidia	16GB	5	110.2
22	M4 Max 128GBapple	128GB	5	72.5
23	M4 Pro 48GBapple	48GB	5	20.3
24	M5 Max 64GBapple	64GB	4	97.0
25	RX 9070amd	16GB	4	73.0
26	M1 Max 64GBapple	64GB	4	21.0
27	M3 Ultra 512GBapple	512GB	4	20.0
28	M5 Pro 64GBapple	64GB	4	20.0
29	RTX 4080nvidia	16GB	3	56.5
30	M2 Ultra 192GBapple	192GB	3	28.0
31	H200nvidia	141GB	3	4.8
32	V100 16GBnvidia	16GB	2	219.1
33	M5 Pro 48GBapple	48GB	2	44.0
34	M3 Ultra 256GBapple	256GB	2	37.4
35	M4 Max 64GBapple	64GB	2	36.0
36	M1 Ultra 128GBapple	128GB	2	31.2
37	M4 32GBapple	32GB	2	22.0
38	RTX A6000 48GBnvidia	48GB	2	17.2
39	M1 Max 32GBapple	32GB	2	15.8
40	M3 Max 96GBapple	96GB	2	12.7
41	M3 Max 128GBapple	128GB	2	5.5
42	M5 32GBapple	32GB	2	1.0
43	RTX 3090 Tinvidia	24GB	1	100.0
44	RTX 4080 Supernvidia	16GB	1	59.0
45	A100 80GBnvidia	80GB	1	56.8
46	RX 7900 GRE 16GBamd	16GB	1	51.9
47	RTX Pro 4500 Blackwell 32GBnvidia	32GB	1	45.2
48	RX 6800 16GBamd	16GB	1	45.0
49	M3 Ultra 192GBapple	192GB	1	43.0
50	M3 Max 48GBapple	48GB	1	38.0
51	M4 16GBapple	16GB	1	20.0
52	M3 Pro 36GBapple	36GB	1	17.7
53	T4 16GBnvidia	16GB	1	17.6
54	A100 40GBnvidia	40GB	1	16.1
55	M5 16GBapple	16GB	1	9.0
56	M2 Pro 32GBapple	32GB	1	8.6
57	AMD Threadripper 256GBamd	256GB	1	7.5
58	M2 Max 64GBapple	64GB	1	2.0
59	Instinct MI300X 192GBamd	192GB	1	—
60	M3 Ultra 96GBapple	96GB	1	—
61	L4nvidia	24GB	1	—

Models that fit

Qwen3.83100 Qwen3.6485 Gemma 4232 DeepSeek V4 Flash92 Qwen3.532 Qwen2.520 Muse17 Qwen38 Ornith1.56 LFM2.56 Agents-A14 LingBot-World-V21

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What to look for

24GB cards are the sweet spot

RTX 3090s and 4090s (both 24GB) hold a 30B-class model in Q4 with plenty of headroom for an 8–16K context. This is arguably the best price/capability point in local LLM inference today — you get most of the quality of a 70B model at a fraction of the hardware cost.

16GB cards work with tighter quants

An RTX 4060 Ti 16GB or RTX 4070 Ti Super 16GB can run 30B models at Q3/Q4 with shorter contexts, though you'll feel the squeeze with longer prompts. Q3 quants noticeably hurt quality on most models — Q4 is the practical floor.

Frequently asked

What's the best GPU for a 30B local LLM?

RTX 3090 (used) or RTX 4090 (new) — both 24GB — are the standard recommendations. They hold a 30B model in Q4 with headroom for a useful context window and run at 25–50 tokens-per-second on most engines.

Can a 16GB GPU run 30B models?

Yes, with caveats. Q3/Q4 quants of 30B-class models fit in ~14–17GB depending on the architecture. You'll have less context room and may need to lower precision further than ideal. A 24GB card is meaningfully better.

How we rank

Hardware is sorted by the number of community submissions on llamaperf — a proxy for how widely each card is used in practice for local LLM inference. Within that, we surface the fastest tokens-per-second observed on each as a quality signal. Submissions come primarily from r/LocalLLaMA discussions and direct user uploads. Nothing here is sponsored or affiliate-driven.