MacBook Pro M4 Max 36GBOpción económica
36 GB Unified (+4)410 GB/s (+210)
C
Sube la velocidad estimada de decodificación alrededor de un 117%.57.7 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 117%.
~$2,499 MSRP
Can DeepSeek R1 Distill Llama 8B run on MacBook Pro M1 Pro 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
DeepSeek R1 Distill Llama 8B needs ~10.2 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~27 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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
Q4_K_M (Medium quality) — 10.2 GB, 26.6 tok/s, Runs well
10.2 GB required23.0 GB available
44% VRAM used
Fit status
Runs well
Decode
26.6 tok/s
TTFT
7267 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 feelsDeepSeek R1 Distill Llama 8B on MacBook Pro M1 Pro 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 26.6 tok/s decode · 7.3s TTFT (warm) · 67 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 | 26.6 tok/s | 3964 ms | 236K |
| Coding | C | Runs well | 26.6 tok/s | 7267 ms | 236K |
| Agentic Coding | C | Runs well | 26.6 tok/s | 10571 ms | 236K |
| Reasoning | C | Runs well | 26.6 tok/s | 8589 ms | 236K |
| RAG | C | Runs well | 26.6 tok/s | 13214 ms | 236K |
Quantization options
How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on MacBook Pro M1 Pro 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 | C46 |
Q4_K_M | 4 | 4.9 GB | Medium | C46 |
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 DeepSeek R1 Distill Llama 8B on your machine.
Run
lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien DeepSeek R1 Distill Llama 8B
MacBook Pro M4 Max 48GBMejor relación calidad-precio
48 GB Unified (+16)546 GB/s (+346)
C
Sube la velocidad estimada de decodificación alrededor de un 189%.76.8 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 189%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBMejora Apple
64 GB Unified (+32)800 GB/s (+600)
C
Sube la velocidad estimada de decodificación alrededor de un 258%.95.1 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 258%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can MacBook Pro M1 Pro 32GB run DeepSeek R1 Distill Llama 8B?
Yes, MacBook Pro M1 Pro 32GB can run DeepSeek R1 Distill Llama 8B with a C grade (Runs well). Expected decode speed: 26.6 tok/s.
How much VRAM does DeepSeek R1 Distill Llama 8B need?
DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 Distill Llama 8B?
The recommended quantization for DeepSeek R1 Distill Llama 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 Distill Llama 8B run at on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, DeepSeek R1 Distill Llama 8B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7267ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 32GB run DeepSeek R1 Distill Llama 8B for coding?
For coding workloads, DeepSeek R1 Distill Llama 8B on MacBook Pro M1 Pro 32GB receives a C grade with 26.6 tok/s and 236K context.
What context window can DeepSeek R1 Distill Llama 8B use on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, DeepSeek R1 Distill Llama 8B 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 M1 Pro 32GB as fast as VRAM for DeepSeek R1 Distill Llama 8B?
Not always. MacBook Pro M1 Pro 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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