RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+104)
A
Raises estimated decode speed by about 27%.60.4 tok/sデコード
Raises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can DeepSeek R1 Distill 7B run on RTX 2000 Ada Laptop 8GB?
YES — Tight Fit
B69Good
Estimated from fit model
DeepSeek R1 Distill 7B needs ~7.1 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 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, 47.5 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
47.5 tok/s
TTFT
4074 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 2000 Ada 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: 47.5 tok/s decode · 4.1s TTFT (warm) · 119 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 | 47.5 tok/s | 2222 ms | 32K |
| Coding | B | Tight fit | 47.5 tok/s | 4074 ms | 32K |
| Agentic Coding | B | Runs with offload | 47.5 tok/s | 5926 ms | 32K |
| Reasoning | B | Tight fit | 47.5 tok/s | 4815 ms | 32K |
| RAG | B | Runs with offload | 47.5 tok/s | 7408 ms | 32K |
Inference speed
DeepSeek R1 Distill 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for DeepSeek R1 Distill 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 96.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 95.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 95.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 61.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 60.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.9 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 50.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 49.2 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 2000 Ada 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 106%.98 tok/sデコード
Raises estimated decode speed by about 106%.
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 102%.96.1 tok/sデコード
Raises estimated decode speed by about 102%.
Adds memory headroom for longer context windows and future model growth.
〜$599 MSRP
Frequently asked questions
Can RTX 2000 Ada Laptop 8GB run DeepSeek R1 Distill 7B?
Yes, RTX 2000 Ada Laptop 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 47.5 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 2000 Ada Laptop 8GB?
On RTX 2000 Ada Laptop 8GB, DeepSeek R1 Distill 7B achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4074ms using Q4_K_M quantization.
Can RTX 2000 Ada Laptop 8GB run DeepSeek R1 Distill 7B for coding?
For coding workloads, DeepSeek R1 Distill 7B on RTX 2000 Ada Laptop 8GB receives a B grade with 47.5 tok/s and 32K context.
What context window can DeepSeek R1 Distill 7B use on RTX 2000 Ada Laptop 8GB?
On RTX 2000 Ada 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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