Can Llama 3.3 70B Instruct run on H100 NVL 188GB?

YES — Runs Great

C50Usable

Estimated from fit model

Llama 3.3 70B Instruct needs ~70.9 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~148 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 70.9 GB, 148.0 tok/s, Runs well

70.9 GB required188.0 GB available

38% VRAM used

Fit status

Runs well

Decode

148.0 tok/s

TTFT

1308 ms

Safe context

244K

Memory

70.9 GB / 188.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct on H100 NVL 188GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 148.0 tok/s decode · 1.3s TTFT (warm) · 370 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	148.0 tok/s	714 ms	244K
Coding	C	Runs well	148.0 tok/s	1308 ms	244K
Agentic Coding	C	Runs well	148.0 tok/s	1903 ms	244K
Reasoning	C	Runs well	148.0 tok/s	1546 ms	244K
RAG	C	Runs well	148.0 tok/s	2379 ms	244K

Inference speed

Llama 3.3 70B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.3 70B Instruct at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	14.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	9.9	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.7	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.0	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	6.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.7	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.7	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.4	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	D39
Q3_K_S	3	34.3 GB	Low	D40
NVFP4	4	39.2 GB	Medium	C40
Q4_K_M	4	42.7 GB	Medium	C41
Q5_K_M	5	50.4 GB	High	C41
Q6_K	6	57.4 GB	High	C42
Q8_0	8	74.9 GB	Very High	C44
F16Best for your GPU	16	143.5 GB	Maximum	C48

Get started

Copy-paste commands to run Llama 3.3 70B Instruct on your machine.

Run

lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server start

Frequently asked questions

Can H100 NVL 188GB run Llama 3.3 70B Instruct?

Yes, H100 NVL 188GB can run Llama 3.3 70B Instruct with a C grade (Runs well). Expected decode speed: 148.0 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 70.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B Instruct?

The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B Instruct run at on H100 NVL 188GB?

On H100 NVL 188GB, Llama 3.3 70B Instruct achieves approximately 148.0 tokens per second decode speed with a time-to-first-token of 1308ms using Q4_K_M quantization.

Can H100 NVL 188GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on H100 NVL 188GB receives a C grade with 148.0 tok/s and 244K context.

What context window can Llama 3.3 70B Instruct use on H100 NVL 188GB?

On H100 NVL 188GB, Llama 3.3 70B Instruct can safely use up to 244K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for Llama 3.3 70B Instruct

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-h100-nvl-188gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

148.0 tok/s

714 ms

244K

Coding

Runs well

148.0 tok/s

1308 ms

244K

Agentic Coding

Runs well

148.0 tok/s

1903 ms

244K

Reasoning

Runs well

148.0 tok/s

1546 ms

244K

RAG

Runs well

148.0 tok/s

2379 ms

244K

Llama 3.3 70B Instruct inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

14.6

Heavy offload

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

14.1

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

13.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

10.9

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

10.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

9.9

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

7.7

Heavy offload

RTX 5090 32GB

32 GB

Q4_K_M

7.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

7.0

Heavy offload

4× RTX 3060 12GB

48 GB

Q4_K_M

6.2

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

4.7

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

4.0

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

3.6

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.7

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.4

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.3

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.1

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	D39
Q3_K_S	3	34.3 GB	Low	D40
NVFP4	4	39.2 GB	Medium	C40
Q4_K_M	4	42.7 GB	Medium	C41
Q5_K_M	5	50.4 GB	High	C41
Q6_K	6	57.4 GB	High	C42
Q8_0	8	74.9 GB	Very High	C44
F16Best for your GPU	16	143.5 GB	Maximum	C48

Frequently asked questions

Can H100 NVL 188GB run Llama 3.3 70B Instruct?

Yes, H100 NVL 188GB can run Llama 3.3 70B Instruct with a C grade (Runs well). Expected decode speed: 148.0 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 70.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B Instruct?

The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B Instruct run at on H100 NVL 188GB?

On H100 NVL 188GB, Llama 3.3 70B Instruct achieves approximately 148.0 tokens per second decode speed with a time-to-first-token of 1308ms using Q4_K_M quantization.

Can H100 NVL 188GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on H100 NVL 188GB receives a C grade with 148.0 tok/s and 244K context.

What context window can Llama 3.3 70B Instruct use on H100 NVL 188GB?

On H100 NVL 188GB, Llama 3.3 70B Instruct can safely use up to 244K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.