zephyr 7b gemma sft african ultrachat 100k needs ~9.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~26 tok/s.
Operating mode
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
Fit status
Runs well
Decode
25.6 tok/s
TTFT
7550 ms
Safe context
329K
Memory
9.9 GB / 25.9 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 25.6 tok/s | 4118 ms | 329K |
| Coding | C | Runs well | 25.6 tok/s | 7550 ms | 329K |
| Agentic Coding | C | Runs well | 25.6 tok/s | 10981 ms | 329K |
| Reasoning | C | Runs well | 25.6 tok/s | 8922 ms | 329K |
| RAG | C | Runs well | 25.6 tok/s | 13726 ms | 329K |
How zephyr 7b gemma sft african ultrachat 100k (7B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C44 |
Q5_K_M | 5 | 5.0 GB | High | C44 |
Q6_K | 6 | 5.7 GB | High | C45 |
Q8_0 | 8 | 7.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C49 |
Copy-paste commands to run zephyr 7b gemma sft african ultrachat 100k on your machine.
Run
lms load hf-mradermacher--zephyr-7b-gemma-sft-african-ultrachat-100k-gguf && lms server startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 243%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 120%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 243%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run zephyr 7b gemma sft african ultrachat 100k with a C grade (Runs well). Expected decode speed: 25.6 tok/s.
zephyr 7b gemma sft african ultrachat 100k (7B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
The recommended quantization for zephyr 7b gemma sft african ultrachat 100k is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, zephyr 7b gemma sft african ultrachat 100k achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7550ms using Q4_K_M quantization.
For coding workloads, zephyr 7b gemma sft african ultrachat 100k on MacBook Pro M3 Pro 36GB receives a C grade with 25.6 tok/s and 329K context.
On MacBook Pro M3 Pro 36GB, zephyr 7b gemma sft african ultrachat 100k can safely use up to 329K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Pro 36GB 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-mradermacher--zephyr-7b-gemma-sft-african-ultrachat-100k-gguf-on-m3-pro-36gb" 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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