Can Llama 2 7B Chat run on NVIDIA DGX Spark 128GB?

Q: Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Llama 2 7B Chat?

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.

YES — Runs Great

C43Usable

Estimated from fit model

Llama 2 7B Chat needs ~19.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~38 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) — 19.3 GB, 38.4 tok/s, Runs well

19.3 GB required108.8 GB available

18% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5047 ms

Safe context

1.8M

Memory

19.3 GB / 108.8 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsLlama 2 7B Chat 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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	38.4 tok/s	2753 ms	1.8M
Coding	C	Runs well	38.4 tok/s	5047 ms	1.8M
Agentic Coding	C	Runs well	38.4 tok/s	7341 ms	1.8M
Reasoning	C	Runs well	38.4 tok/s	5964 ms	1.8M
RAG	C	Runs well	38.4 tok/s	9176 ms	1.8M

Quantization options

How Llama 2 7B Chat (7B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run Llama 2 7B Chat on your machine.

Run

lms load hf-thebloke--llama-2-7b-chat-gguf && lms server start

Upgrade options

Hardware that runs Llama 2 7B Chat well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 155%.98 tok/s decode

Raises estimated decode speed by about 155%.

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 155%.98 tok/s decode

Raises estimated decode speed by about 155%.

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 155%.98 tok/s decode

Raises estimated decode speed by about 155%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Llama 2 7B Chat?

Yes, NVIDIA DGX Spark 128GB can run Llama 2 7B Chat with a C grade (Runs well). Expected decode speed: 38.4 tok/s.

How much VRAM does Llama 2 7B Chat need?

Llama 2 7B Chat (7B parameters) requires approximately 19.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 2 7B Chat?

The recommended quantization for Llama 2 7B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 2 7B Chat run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 2 7B Chat achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5047ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Llama 2 7B Chat for coding?

For coding workloads, Llama 2 7B Chat on NVIDIA DGX Spark 128GB receives a C grade with 38.4 tok/s and 1.8M context.

What context window can Llama 2 7B Chat use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 2 7B Chat can safely use up to 1.8M 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 Llama 2 7B Chat?

See all results for NVIDIA DGX Spark 128GB See all hardware for Llama 2 7B Chat

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