Best GPUs for 70B local LLMs

70B models in Q4_K_M quantization need roughly 40GB of VRAM plus headroom for context. That puts the realistic floor at a 48GB card (or two 24GB cards), or an Apple Silicon Mac with 64GB+ unified memory. The list below filters to hardware that has actually been reported running models at this scale.

Ranked from 285 community reports on llamaperf.

Ranked by community reports

#	GPU	VRAM	Reports	Fastest t/s
1	RTX 5090nvidia	32GB	67	578.0
2	AMD Strix Halo 128GBamd	128GB	35	153.3
3	RTX Pro 6000 Blackwellnvidia	96GB	27	177.0
4	DGX Sparknvidia	128GB	25	181.0
5	M5 Max 128GBapple	128GB	18	133.6
6	Radeon AI PRO R9700 32GBamd	32GB	17	280.0
7	RTX PRO 6000 Max-Qnvidia	96GB	6	240.0
8	V100 32GBnvidia	32GB	6	218.0
9	M2 Max 96GBapple	96GB	6	43.0
10	AMD MI50 32GBamd	32GB	6	15.5
11	RTX 6000nvidia	48GB	5	150.0
12	M4 Max 128GBapple	128GB	5	72.5
13	M4 Pro 48GBapple	48GB	5	20.3
14	H100 80GBnvidia	80GB	4	193.0
15	M5 Max 64GBapple	64GB	4	97.0
16	M1 Max 64GBapple	64GB	4	21.0
17	M3 Ultra 512GBapple	512GB	4	20.0
18	M5 Pro 64GBapple	64GB	4	20.0
19	M2 Ultra 192GBapple	192GB	3	28.0
20	H200nvidia	141GB	3	4.8
21	M5 Pro 48GBapple	48GB	2	44.0
22	M3 Ultra 256GBapple	256GB	2	37.4
23	M4 Max 64GBapple	64GB	2	36.0
24	M1 Ultra 128GBapple	128GB	2	31.2
25	M4 32GBapple	32GB	2	22.0
26	RTX A6000 48GBnvidia	48GB	2	17.2
27	M1 Max 32GBapple	32GB	2	15.8
28	M3 Max 96GBapple	96GB	2	12.7
29	M3 Max 128GBapple	128GB	2	5.5
30	M5 32GBapple	32GB	2	1.0
31	A100 80GBnvidia	80GB	1	56.8
32	RTX Pro 4500 Blackwell 32GBnvidia	32GB	1	45.2
33	M3 Ultra 192GBapple	192GB	1	43.0
34	M3 Max 48GBapple	48GB	1	38.0
35	M3 Pro 36GBapple	36GB	1	17.7
36	A100 40GBnvidia	40GB	1	16.1
37	M2 Pro 32GBapple	32GB	1	8.6
38	AMD Threadripper 256GBamd	256GB	1	7.5
39	M2 Max 64GBapple	64GB	1	2.0
40	Instinct MI300X 192GBamd	192GB	1	—
41	M3 Ultra 96GBapple	96GB	1	—

Models that fit

Qwen3.82781 Qwen3.6485 Gemma 4232 DeepSeek V4 Flash92 Qwen3.532 Qwen2.520 Muse17 Qwen38 Ornith1.56 LFM2.56 Agents-A14 LingBot-World-V21

No reports yet

These match the profile but nobody has submitted a report yet.

M5 Ultra 512GB M5 Ultra 256GB Instinct MI250X 128GB M2 Ultra 128GB M4 Max 96GB M5 Max 96GB M3 Max 64GB M1 Ultra 64GB M4 Pro 64GB M2 Ultra 64GB M4 Max 48GB M5 Max 48GB M4 Max 36GB M3 Max 36GB M2 Max 32GB M1 Pro 32GB

What to look for

VRAM math for 70B models

At Q4_K_M, a 70B model is roughly 40–43GB of weights. At Q5/Q6 you're at 48–55GB. KV cache scales with context length (a 4K context on a 70B model adds another ~3GB at full precision; less with KV quantization). Plan for 48GB minimum for comfortable Q4, 64GB+ for higher quants or longer contexts.

Single big card vs multi-card

An RTX A6000 (48GB) or H100 (80GB) holds the whole model in one device — no inter-GPU communication overhead. Two RTX 3090s with NVLink approach this but pay a small latency penalty for tensor-parallel split. For interactive use both work; for serving at scale, single-card is simpler.

Apple Silicon as a 70B host

M2 Ultra and M3 Ultra Macs with 128GB+ run 70B models well, often at 5–12 tokens-per-second depending on quant. The advantage is total cost — a Mac Studio Ultra is competitive in price with a single H100 and runs the same model class without datacenter cooling.

Frequently asked

What's the minimum VRAM for a 70B local LLM?

Around 40GB at Q4 quantization, but practical use needs 48GB or more once you account for context and KV cache. Below that, you'll need to offload layers to system memory which sharply degrades tokens-per-second.

Can I run a 70B model on a single RTX 4090?

Not well. A 24GB card requires aggressive quantization (Q2/Q3) or offloading large portions to CPU memory, both of which degrade quality and speed substantially. Two 3090s/4090s, or a single A6000/H100, is the standard solution.

Is a Mac Studio Ultra good for 70B models?

Yes — M-Ultra Macs with 128GB+ unified memory run 70B models comfortably with no special setup. Throughput is lower than a discrete A100/H100 but the total cost is much lower.

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.