Will It Run AI

Can Llama 3 8B Instruct 32k v0.1 run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable
Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~11.8 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 11.8 GB, 95.0 tok/s, Runs well
11.8 GB required48.0 GB available
25% VRAM used

Fit status

Runs well

Decode

95.0 tok/s

TTFT

2038 ms

Safe context

634K

Memory

11.8 GB / 48.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on Quadro RTX 8000 48GB
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: 95.0 tok/s decode · 2.0s TTFT (warm) · 238 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well95.0 tok/s1111 ms634K
CodingCRuns well95.0 tok/s2038 ms634K
Agentic CodingCRuns well95.0 tok/s2964 ms634K
ReasoningCRuns well95.0 tok/s2408 ms634K
RAGCRuns well95.0 tok/s3705 ms634K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC42
Q3_K_S
3
3.9 GB
LowC42
NVFP4
4
4.5 GB
MediumC42
Q4_K_M
4
4.9 GB
MediumC42
Q5_K_M
5
5.8 GB
HighC42
Q6_K
6
6.6 GB
HighC42
Q8_0
8
8.6 GB
Very HighC43
F16Best for your GPU
16
16.4 GB
MaximumC45

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

Upgrade-Optionen

Hardware, die Llama 3 8B Instruct 32k v0.1 gut ausführt

Mac Studio M3 Ultra 96GBGünstige Wahl
96 GB Unified (+48)819 GB/s (+147)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s Dekodierung

Adds memory headroom for longer context windows and future model growth.

ca. $3,999 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run Llama 3 8B Instruct 32k v0.1?

Yes, Quadro RTX 8000 48GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 95.0 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 11.8 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 95.0 tokens per second decode speed with a time-to-first-token of 2038ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on Quadro RTX 8000 48GB receives a C grade with 95.0 tok/s and 634K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GBSee all hardware for Llama 3 8B Instruct 32k v0.1
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