willitrun·ai

Can Llama 3 8B Instruct 32k v0.1 run on NVIDIA H100 80GB?

YES — Runs Great

C46Usable
Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~15.0 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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) — 15.0 GB, 112.0 tok/s, Runs well
15.0 GB required80.0 GB available
19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

1.1M

Memory

15.0 GB / 80.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on NVIDIA H100 80GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well112.0 tok/s943 ms1.1M
CodingCRuns well112.0 tok/s1729 ms1.1M
Agentic CodingCRuns well112.0 tok/s2514 ms1.1M
ReasoningCRuns well112.0 tok/s2043 ms1.1M
RAGCRuns well112.0 tok/s3143 ms1.1M

Inference speed

Llama 3 8B Instruct 32k v0.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3 8B Instruct 32k v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M112.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M112.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M112.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M112.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M112.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M112.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M95.1Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M90.2Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M77.5Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M76.8Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M76.8Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M49.2Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M48.7Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M45.1Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M40.7Offloads
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M39.6Fits

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 8B Instruct 32k v0.1 (8B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowD40
Q3_K_S
3
3.9 GB
LowD40
NVFP4
4
4.5 GB
MediumD40
Q4_K_M
4
4.9 GB
MediumD40
Q5_K_M
5
5.8 GB
HighD40
Q6_K
6
6.6 GB
HighD40
Q8_0
8
8.6 GB
Very HighC40
F16Best for your GPU
16
16.4 GB
MaximumC41

Get started

Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

アップグレードオプション

Llama 3 8B Instruct 32k v0.1を快適に動かすハードウェア

Mac Studio M2 Ultra 128GBコスパ重視
128 GB Unified (+48)
C
This setup is broadly balanced for this model.95.1 tok/sデコード

〜$3,999 MSRP

Mac Studio M1 Ultra 128GBコスパ最良
128 GB Unified (+48)
C
This setup is broadly balanced for this model.90.2 tok/sデコード

〜$3,999 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run Llama 3 8B Instruct 32k v0.1?

Yes, NVIDIA H100 80GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3 8B Instruct 32k v0.1 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on NVIDIA H100 80GB receives a C grade with 112.0 tok/s and 1.1M context.

What context window can Llama 3 8B Instruct 32k v0.1 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GBSee all hardware for Llama 3 8B Instruct 32k v0.1
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