Can LLaVA 1.5 7B run on Radeon Pro W6800 32GB?

YES — Runs Great

B70Good

Estimated from fit model

LLaVA 1.5 7B needs ~16.2 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~67 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 16.2 GB, 67.1 tok/s, Runs well

16.2 GB required32.0 GB available

51% VRAM used

Fit status

Runs well

Decode

67.1 tok/s

TTFT

2883 ms

Safe context

Memory

16.2 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsLLaVA 1.5 7B on Radeon Pro W6800 32GB
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: 67.1 tok/s decode · 2.9s TTFT (warm) · 168 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	67.1 tok/s	1573 ms	4K
Coding	B	Runs well	67.1 tok/s	2883 ms	4K
Agentic Coding	A	Runs well	67.1 tok/s	4194 ms	4K
Reasoning	B	Runs well	67.1 tok/s	3407 ms	4K
RAG	A	Runs well	67.1 tok/s	5242 ms	4K

Quantization options

How LLaVA 1.5 7B (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B61
Q3_K_S	3	3.4 GB	Low	B61
NVFP4	4

Get started

Copy-paste commands to run LLaVA 1.5 7B on your machine.

Run

ollama run llava

Upgrade options

Hardware that runs LLaVA 1.5 7B well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)546 GB/s (+34)

Raises estimated decode speed by about 31%.87.8 tok/s decode

Raises estimated decode speed by about 31%.

~$2,499 MSRP

Frequently asked questions

Can Radeon Pro W6800 32GB run LLaVA 1.5 7B?

Yes, Radeon Pro W6800 32GB can run LLaVA 1.5 7B with a B grade (Runs well). Expected decode speed: 67.1 tok/s.

How much VRAM does LLaVA 1.5 7B need?

LLaVA 1.5 7B (7B parameters) requires approximately 16.2 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.5 7B?

The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.5 7B run at on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, LLaVA 1.5 7B achieves approximately 67.1 tokens per second decode speed with a time-to-first-token of 2883ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on Radeon Pro W6800 32GB receives a B grade with 67.1 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, LLaVA 1.5 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for Radeon Pro W6800 32GB See all hardware for LLaVA 1.5 7B

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