MacBook Pro M4 Max 96GBOpção econômica
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.21 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Can DeepSeek R1 1.5B run on NVIDIA L40S 48GB?
YES — Runs Great
C51Usable
Estimated from fit model
DeepSeek R1 1.5B needs ~7.0 GB VRAM. NVIDIA L40S 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 7.0 GB, 24.0 tok/s, Runs well
7.0 GB required48.0 GB available
15% VRAM used
Fit status
Runs well
Decode
24.0 tok/s
TTFT
8067 ms
Safe context
33K
Memory
7.0 GB / 48.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 1.5B on NVIDIA L40S 48GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 24.0 tok/s | 4400 ms | 33K |
| Coding | C | Runs well | 24.0 tok/s | 8067 ms | 33K |
| Agentic Coding | C | Runs well | 24.0 tok/s | 11733 ms | 33K |
| Reasoning | C | Runs well | 24.0 tok/s | 9533 ms | 33K |
| RAG | C | Runs well | 24.0 tok/s | 14667 ms | 33K |
Quantization options
How DeepSeek R1 1.5B (1.5B params) fits at each quantization level on NVIDIA L40S 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C52 |
Q3_K_S | 3 | 0.7 GB | Low | C52 |
NVFP4 | 4 | 0.8 GB | Medium | C52 |
Q4_K_M | 4 | 0.9 GB | Medium | C52 |
Q5_K_M | 5 | 1.1 GB | High | C52 |
Q6_K | 6 | 1.2 GB | High | C52 |
Q8_0 | 8 | 1.6 GB | Very High | C52 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C52 |
Get started
Copy-paste commands to run DeepSeek R1 1.5B on your machine.
Run
ollama run deepseek-r1:1.5bOpções de upgrade
Hardware que roda bem DeepSeek R1 1.5B
Frequently asked questions
Can NVIDIA L40S 48GB run DeepSeek R1 1.5B?
Yes, NVIDIA L40S 48GB can run DeepSeek R1 1.5B with a C grade (Runs well). Expected decode speed: 24.0 tok/s.
How much VRAM does DeepSeek R1 1.5B need?
DeepSeek R1 1.5B (1.5B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 1.5B?
The recommended quantization for DeepSeek R1 1.5B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 1.5B run at on NVIDIA L40S 48GB?
On NVIDIA L40S 48GB, DeepSeek R1 1.5B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.
Can NVIDIA L40S 48GB run DeepSeek R1 1.5B for coding?
For coding workloads, DeepSeek R1 1.5B on NVIDIA L40S 48GB receives a C grade with 24.0 tok/s and 33K context.
What context window can DeepSeek R1 1.5B use on NVIDIA L40S 48GB?
On NVIDIA L40S 48GB, DeepSeek R1 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-qwen-1.5b-on-l40s-48gb" 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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