12 GB VRAM (+4)360 GB/s (+104)
A
Adds memory headroom for longer context windows and future model growth.60.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can DeepSeek R1 Distill 7B run on RTX 4060 Laptop 8GB?
YES — Tight Fit
B69Good
○Estimated from fit model
DeepSeek R1 Distill 7B needs ~7.1 GB VRAM. RTX 4060 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
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
Q4_K_M (Medium quality) — 7.1 GB, 48.8 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
48.8 tok/s
TTFT
3966 ms
Safe context
32K
Memory
7.1 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill 7B on RTX 4060 Laptop 8GB
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: 48.8 tok/s decode · 4.0s TTFT (warm) · 122 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 | B | Tight fit | 48.8 tok/s | 2163 ms | 32K |
| Coding | B | Tight fit | 48.8 tok/s | 3966 ms | 32K |
| Agentic Coding | B | Runs with offload | 48.8 tok/s | 5768 ms | 32K |
| Reasoning | B | Tight fit | 48.8 tok/s | 4687 ms | 32K |
| RAG | B | Runs with offload | 48.8 tok/s | 7210 ms | 32K |
Quantization options
How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4060 Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A70 |
Q3_K_S | 3 | 3.4 GB | Low | A71 |
NVFP4 | 4 | 3.9 GB | Medium | A70 |
Q4_K_M | 4 | 4.3 GB | Medium | A70 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | B70 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.
Run
ollama run deepseek-r1:7bUpgrade options
Hardware that runs DeepSeek R1 Distill 7B well
12 GB VRAM (+4)672 GB/s (+416)
A
Raises estimated decode speed by about 101%.98 tok/s decode
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
12 GB VRAM (+4)504 GB/s (+248)
A
Raises estimated decode speed by about 97%.96.1 tok/s decode
Raises estimated decode speed by about 97%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP