Can mxbai Embed Large run on Radeon Pro W6800 32GB?
YES — Runs Great
A71Great○Estimated from fit model
mxbai Embed Large needs ~6.1 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With F16 quantization, expect ~5 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Memory bandwidth
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.
Select quantization to explore
F16 (Maximum quality) — 6.6 GB, 4.7 tok/s, Runs well
6.6 GB required32.0 GB available
Memory breakdown
Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsmxbai Embed Large 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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 tok/s prefill
What limits this setup
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 4.7 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Best improvement path
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|
| Chat | A | Runs well | 4.7 tok/s | 22516 ms | 512 |
| Coding | A | Runs well | 4.7 tok/s | 41279 ms | 512 |
| Agentic Coding | A | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | A | Runs well | 4.7 tok/s | 48785 ms | 512 |
| RAG | A | Runs well | 4.7 tok/s | 75053 ms | 512 |
Quantization options
How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|
Q2_K | 2 | 0.1 GB | Low | A77 |
Q3_K_S | 3 | 0.2 GB | Low | A77 |
NVFP4 | 4 |
Get started
Copy-paste commands to run mxbai Embed Large on your machine.
Run
ollama run mxbai-embed-large
Your hardware
More models your Radeon Pro W6800 32GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|
👁 Alibaba
Qwen3-Coder 30B A3B Instruct | 30.5B | S | 43.4 tok/s |
👁 Alibaba
Qwen 3.5 27B | 27B | S | 18.8 tok/s |
Frequently asked questions
F16Best for your GPU | 16 | 0.7 GB | Maximum | A77 |
👁 Alibaba
Qwen3-VL 30B A3B Instruct | 30B | S | 44.8 tok/s |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
