RTX 3060 12GBGünstige Wahl12 GB VRAM (+4)360 GB/s (+104)
A
Adds memory headroom for longer context windows and future model growth.60.4 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $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
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-Optionen
Hardware, die DeepSeek R1 Distill 7B gut ausführt
RTX 3060 12GBGünstige WahlRTX 5070 12GBBestes Preis-Leistungs-Verhältnis12 GB VRAM (+4)672 GB/s (+416)
A
Raises estimated decode speed by about 101%.98 tok/s Dekodierung
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
ca. $549 MSRP
RTX 4070 12GBNVIDIA-Upgrade12 GB VRAM (+4)504 GB/s (+248)
A
Raises estimated decode speed by about 97%.96.1 tok/s Dekodierung
Raises estimated decode speed by about 97%.
Adds memory headroom for longer context windows and future model growth.
ca. $599 MSRP
Frequently asked questions
Can RTX 4060 Laptop 8GB run DeepSeek R1 Distill 7B?
Yes, RTX 4060 Laptop 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 48.8 tok/s.
How much VRAM does DeepSeek R1 Distill 7B need?
DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 Distill 7B?
The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 Distill 7B run at on RTX 4060 Laptop 8GB?
On RTX 4060 Laptop 8GB, DeepSeek R1 Distill 7B achieves approximately 48.8 tokens per second decode speed with a time-to-first-token of 3966ms using Q4_K_M quantization.
Can RTX 4060 Laptop 8GB run DeepSeek R1 Distill 7B for coding?
For coding workloads, DeepSeek R1 Distill 7B on RTX 4060 Laptop 8GB receives a B grade with 48.8 tok/s and 32K context.
What context window can DeepSeek R1 Distill 7B use on RTX 4060 Laptop 8GB?
On RTX 4060 Laptop 8GB, DeepSeek R1 Distill 7B can safely use up to 32K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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