RX 7900 XTX 24GBOpción económica
960 GB/s (+560)
C
Sube la velocidad estimada de decodificación alrededor de un 136%.112 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 136%.
~$999 MSRP
Can openchat 3.6 8b 20240522 IMat run on MacBook Pro M2 Max 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
openchat 3.6 8b 20240522 IMat needs ~10.2 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 10.2 GB, 47.5 tok/s, Runs well
10.2 GB required23.0 GB available
44% VRAM used
Fit status
Runs well
Decode
47.5 tok/s
TTFT
4072 ms
Safe context
236K
Memory
10.2 GB / 23.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsopenchat 3.6 8b 20240522 IMat on MacBook Pro M2 Max 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: 47.5 tok/s decode · 4.1s TTFT (warm) · 119 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 | 47.5 tok/s | 2221 ms | 236K |
| Coding | C | Runs well | 47.5 tok/s | 4072 ms | 236K |
| Agentic Coding | C | Runs well | 47.5 tok/s | 5923 ms | 236K |
| Reasoning | C | Runs well | 47.5 tok/s | 4813 ms | 236K |
| RAG | C | Runs well | 47.5 tok/s | 7404 ms | 236K |
Quantization options
How openchat 3.6 8b 20240522 IMat (8B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C45 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C47 |
Q8_0 | 8 | 8.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
Get started
Copy-paste commands to run openchat 3.6 8b 20240522 IMat on your machine.
Run
lms load hf-legraphista--openchat-3-6-8b-20240522-imat-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien openchat 3.6 8b 20240522 IMat
Frequently asked questions
Can MacBook Pro M2 Max 32GB run openchat 3.6 8b 20240522 IMat?
Yes, MacBook Pro M2 Max 32GB can run openchat 3.6 8b 20240522 IMat with a C grade (Runs well). Expected decode speed: 47.5 tok/s.
How much VRAM does openchat 3.6 8b 20240522 IMat need?
openchat 3.6 8b 20240522 IMat (8B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.
What is the best quantization for openchat 3.6 8b 20240522 IMat?
The recommended quantization for openchat 3.6 8b 20240522 IMat is Q4_K_M, which balances quality and memory efficiency.
What speed will openchat 3.6 8b 20240522 IMat run at on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, openchat 3.6 8b 20240522 IMat achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4072ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 32GB run openchat 3.6 8b 20240522 IMat for coding?
For coding workloads, openchat 3.6 8b 20240522 IMat on MacBook Pro M2 Max 32GB receives a C grade with 47.5 tok/s and 236K context.
What context window can openchat 3.6 8b 20240522 IMat use on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, openchat 3.6 8b 20240522 IMat can safely use up to 236K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for openchat 3.6 8b 20240522 IMat?
Not always. MacBook Pro M2 Max 32GB 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.
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<iframe src="https://willitrunai.com/embed/hf-legraphista--openchat-3-6-8b-20240522-imat-gguf-on-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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