Can Apertus v1.5 70B run on NVIDIA H100 80GB?

YES — Runs Great

A85Great

Estimated from fit model

Apertus v1.5 70B needs ~57.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 57.7 GB, 69.7 tok/s, Runs well

57.7 GB required80.0 GB available

72% VRAM used

Fit status

Runs well

Decode

69.7 tok/s

TTFT

2779 ms

Safe context

89K

Memory

57.7 GB / 80.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsApertus v1.5 70B on NVIDIA H100 80GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 69.7 tok/s decode · 2.8s TTFT (warm) · 174 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	A	Runs well	64.1 tok/s	1648 ms	89K
Coding	A	Runs well	64.1 tok/s	3022 ms	89K
Agentic Coding	A	Runs well	64.1 tok/s	4395 ms	89K
Reasoning	A	Runs well	64.1 tok/s	3571 ms	89K
RAG	A	Runs well	64.1 tok/s	5494 ms	89K

Inference speed

Apertus v1.5 70B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Apertus v1.5 70B 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 ~17 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	16.7	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	8.8	Heavy offload
2× RTX 3090 24GB	48 GB	Q4_K_M	8.1	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.1	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	5.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.4	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	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 Apertus v1.5 70B (72B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	10.4 GB	Very Low	B70
Q2_0_G128	1.71	19.2 GB	Low	A71
Q2_K	2	28.1 GB	Low	A73
Q3_K_S	3	35.3 GB	Low	A75
NVFP4	4	40.3 GB	Medium	A76
Q4_K_M	4	43.9 GB	Medium	A77
Q5_K_M	5	51.8 GB	High	A77
Q6_KBest for your GPU	6	59.0 GB	High	A77
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Get started

Copy-paste commands to run Apertus v1.5 70B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "swiss-ai/Apertus-v1.5-70B" \
  --hf-file "Apertus-v1.5-70B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	29 tok/s
Qwen 3.5 122B A10B	122B	A	86 tok/s
Command A 111B	111B	S	38.3 tok/s
Pixtral Large 124B	124B	A	28.5 tok/s
GPT-OSS 120B	117B	A	33 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run Apertus v1.5 70B?

Yes, NVIDIA H100 80GB can run Apertus v1.5 70B with a A grade (Runs well). Expected decode speed: 64.1 tok/s.

How much VRAM does Apertus v1.5 70B need?

Apertus v1.5 70B (72B parameters) requires approximately 57.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Apertus v1.5 70B?

The recommended quantization for Apertus v1.5 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Apertus v1.5 70B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Apertus v1.5 70B achieves approximately 64.1 tokens per second decode speed with a time-to-first-token of 3022ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Apertus v1.5 70B for coding?

For coding workloads, Apertus v1.5 70B on NVIDIA H100 80GB receives a A grade with 64.1 tok/s and 89K context.

What context window can Apertus v1.5 70B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Apertus v1.5 70B can safely use up to 89K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Apertus v1.5 70B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/apertus-v1.5-70b-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: