Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can All MiniLM L6 v2 run on AMD Instinct MI300A 128GB?
YES — Runs Great
B60Good
○Estimated from fit model
All MiniLM L6 v2 needs ~14.3 GB VRAM. AMD Instinct MI300A 128GB has 128.0 GB. With F16 quantization, expect ~2 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Memory bandwidth
Share:
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) — 14.3 GB, 2.0 tok/s, Runs well
14.3 GB required128.0 GB available
11% VRAM used
Fit status
Runs well
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
256
Memory
14.3 GB / 128.0 GB
Memory breakdown
Weights0.0 GB
KV Cache0.3 GB
Runtime1.2 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsAll MiniLM L6 v2 on AMD Instinct MI300A 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 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 2.0 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 | B | Runs well | 2.0 tok/s | 52800 ms | 256 |
| Coding | B | Runs well | 2.0 tok/s | 96800 ms | 256 |
| Agentic Coding | B | Runs well | 2.0 tok/s | 140800 ms | 256 |
| Reasoning | B | Runs well | 2.0 tok/s | 114400 ms | 256 |
| RAG | B | Runs well | 2.0 tok/s | 176000 ms | 256 |
Quantization options
How All MiniLM L6 v2 (0.023000000044703484B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | B65 |
Q3_K_S | 3 | 0.0 GB | Low | B65 |
NVFP4 | 4 | 0.0 GB | Medium | B65 |
Q4_K_M | 4 | 0.0 GB | Medium | B65 |
Q5_K_M | 5 | 0.0 GB | High | B65 |
Q6_K | 6 | 0.0 GB | High | B65 |
Q8_0 | 8 | 0.0 GB | Very High | B65 |
F16Best for your GPU | 16 | 0.0 GB | Maximum | B65 |
Get started
Copy-paste commands to run All MiniLM L6 v2 on your machine.
Run
ollama run all-minilmUpgrade options