Can llava llama 3 8b v1 1 run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C43Usable

Estimated from fit model

llava llama 3 8b v1 1 needs ~20.1 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 20.1 GB, 33.6 tok/s, Runs well

20.1 GB required108.8 GB available

18% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5768 ms

Safe context

1.5M

Memory

20.1 GB / 108.8 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsllava llama 3 8b v1 1 on NVIDIA DGX Spark 128GB
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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	33.6 tok/s	3146 ms	1.5M
Coding	C	Runs well	33.6 tok/s	5768 ms	1.5M
Agentic Coding	C	Runs well	33.6 tok/s	8390 ms	1.5M
Reasoning	C	Runs well	33.6 tok/s	6817 ms	1.5M
RAG	C	Runs well	33.6 tok/s	10487 ms	1.5M

Quantization options

How llava llama 3 8b v1 1 (8B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	D39
Q3_K_S	3	3.9 GB	Low	D39
NVFP4	4	4.5 GB	Medium	D40
Q4_K_M	4	4.9 GB	Medium	D40
Q5_K_M	5	5.8 GB	High	D40
Q6_K	6	6.6 GB	High	D40
Q8_0	8	8.6 GB	Very High	D40
F16Best for your GPU	16	16.4 GB	Maximum	C41

Get started

Copy-paste commands to run llava llama 3 8b v1 1 on your machine.

Run

lms load hf-xtuner--llava-llama-3-8b-v1-1-gguf && lms server start

Upgrade options

Hardware that runs llava llama 3 8b v1 1 well

NVIDIA H200 141GBBudget pick

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 233%.112 tok/s decode

Raises estimated decode speed by about 233%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBBest value

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 233%.112 tok/s decode

Raises estimated decode speed by about 233%.

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

~$30,000 MSRP

NVIDIA B200 180GBNVIDIA upgrade

180 GB VRAM (+52)8000 GB/s (+7727)

Raises estimated decode speed by about 233%.112 tok/s decode

Raises estimated decode speed by about 233%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run llava llama 3 8b v1 1?

Yes, NVIDIA DGX Spark 128GB can run llava llama 3 8b v1 1 with a C grade (Runs well). Expected decode speed: 33.6 tok/s.

How much VRAM does llava llama 3 8b v1 1 need?

llava llama 3 8b v1 1 (8B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.

What is the best quantization for llava llama 3 8b v1 1?

The recommended quantization for llava llama 3 8b v1 1 is Q4_K_M, which balances quality and memory efficiency.

What speed will llava llama 3 8b v1 1 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, llava llama 3 8b v1 1 achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5768ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run llava llama 3 8b v1 1 for coding?

For coding workloads, llava llama 3 8b v1 1 on NVIDIA DGX Spark 128GB receives a C grade with 33.6 tok/s and 1.5M context.

What context window can llava llama 3 8b v1 1 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, llava llama 3 8b v1 1 can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for llava llama 3 8b v1 1?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for llava llama 3 8b v1 1

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<iframe src="https://willitrunai.com/embed/hf-xtuner--llava-llama-3-8b-v1-1-gguf-on-dgx-spark-128gb" 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

33.6 tok/s

3146 ms

1.5M

Coding

Runs well

33.6 tok/s

5768 ms

1.5M

Agentic Coding

Runs well

33.6 tok/s

8390 ms

1.5M

Reasoning

Runs well

33.6 tok/s

6817 ms

1.5M

RAG

Runs well

33.6 tok/s

10487 ms

1.5M

Quantization options

How llava llama 3 8b v1 1 (8B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	D39
Q3_K_S	3	3.9 GB	Low	D39
NVFP4	4	4.5 GB	Medium	D40
Q4_K_M	4	4.9 GB	Medium	D40
Q5_K_M	5	5.8 GB	High	D40
Q6_K	6	6.6 GB	High	D40
Q8_0	8	8.6 GB	Very High	D40
F16Best for your GPU	16	16.4 GB	Maximum	C41