Can Llama 3 8B Instruct 32k v0.1 run on Quadro RTX 6000 24GB?

YES — Runs Great

C50Usable

Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~9.4 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.4 GB, 95.0 tok/s, Runs well

9.4 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

95.0 tok/s

TTFT

2038 ms

Safe context

265K

Memory

9.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on Quadro RTX 6000 24GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	95.0 tok/s	1111 ms	265K
Coding	C	Runs well	95.0 tok/s	2038 ms	265K
Agentic Coding	C	Runs well	95.0 tok/s	2964 ms	265K
Reasoning	C	Runs well	95.0 tok/s	2408 ms	265K
RAG	C	Runs well	95.0 tok/s	3705 ms	265K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

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

Frequently asked questions

Can Quadro RTX 6000 24GB run Llama 3 8B Instruct 32k v0.1?

Yes, Quadro RTX 6000 24GB 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 9.4 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 6000 24GB?

On Quadro RTX 6000 24GB, 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 6000 24GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on Quadro RTX 6000 24GB receives a C grade with 95.0 tok/s and 265K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 265K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 6000 24GB See all hardware for Llama 3 8B Instruct 32k v0.1

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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-quadro-rtx-6000-24gb" 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

95.0 tok/s

1111 ms

265K

Coding

Runs well

95.0 tok/s

2038 ms

265K

Agentic Coding

Runs well

95.0 tok/s

2964 ms

265K

Reasoning

Runs well

95.0 tok/s

2408 ms

265K

RAG

Runs well

95.0 tok/s

3705 ms

265K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50