RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+104)
A
Adds memory headroom for longer context windows and future model growth.60.4 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can DeepSeek R1 Distill 7B run on RTX 4070 Laptop 8GB?
YES — Tight Fit
B69Good
Estimated from fit model
DeepSeek R1 Distill 7B needs ~7.1 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~50 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, 49.5 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
49.5 tok/s
TTFT
3913 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 4070 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: 49.5 tok/s decode · 3.9s TTFT (warm) · 124 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 | 49.5 tok/s | 2135 ms | 32K |
| Coding | B | Tight fit | 49.5 tok/s | 3913 ms | 32K |
| Agentic Coding | B | Runs with offload | 49.5 tok/s | 5692 ms | 32K |
| Reasoning | B | Tight fit | 49.5 tok/s | 4625 ms | 32K |
| RAG | B | Runs with offload | 49.5 tok/s | 7115 ms | 32K |
Quantization options
How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4070 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-r1:7bアップグレードオプション
DeepSeek R1 Distill 7Bを快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5070 12GBコスパ最良12 GB VRAM (+4)672 GB/s (+416)
A
Raises estimated decode speed by about 98%.98 tok/sデコード
Raises estimated decode speed by about 98%.
Adds memory headroom for longer context windows and future model growth.
〜$549 MSRP
RTX 4070 12GBNVIDIAアップグレード12 GB VRAM (+4)504 GB/s (+248)
A
Raises estimated decode speed by about 94%.96.1 tok/sデコード
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
〜$599 MSRP
Frequently asked questions
Can RTX 4070 Laptop 8GB run DeepSeek R1 Distill 7B?
Yes, RTX 4070 Laptop 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 49.5 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 4070 Laptop 8GB?
On RTX 4070 Laptop 8GB, DeepSeek R1 Distill 7B achieves approximately 49.5 tokens per second decode speed with a time-to-first-token of 3913ms using Q4_K_M quantization.
Can RTX 4070 Laptop 8GB run DeepSeek R1 Distill 7B for coding?
For coding workloads, DeepSeek R1 Distill 7B on RTX 4070 Laptop 8GB receives a B grade with 49.5 tok/s and 32K context.
What context window can DeepSeek R1 Distill 7B use on RTX 4070 Laptop 8GB?
On RTX 4070 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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