RX 7900 XT 20GBBest value
20 GB VRAM (+4)800 GB/s (+512)
B
Raises estimated decode speed by about 83%.98 tok/s decode
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can DeepSeek R1 Distill 7B run on RTX 4060 Ti 16GB?
YES — Runs Great
B67Good
○Estimated from fit model
DeepSeek R1 Distill 7B needs ~7.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 7.9 GB, 53.5 tok/s, Runs well
7.9 GB required16.0 GB available
49% VRAM used
Fit status
Runs well
Decode
53.5 tok/s
TTFT
3622 ms
Safe context
33K
Memory
7.9 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill 7B on RTX 4060 Ti 16GB
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: 53.5 tok/s decode · 3.6s TTFT (warm) · 134 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 | Runs well | 49.2 tok/s | 2145 ms | 33K |
| Coding | B | Runs well | 49.2 tok/s | 3932 ms | 33K |
| Agentic Coding | B | Runs well | 49.2 tok/s | 5719 ms | 33K |
| Reasoning | B | Runs well | 49.2 tok/s | 4647 ms | 33K |
| RAG | B | Runs well | 49.2 tok/s | 7149 ms | 33K |
Quantization options
How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B64 |
Q3_K_S | 3 | 3.4 GB | Low | B64 |
NVFP4 | 4 |
Get started
Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.
Run
ollama run deepseek-r1:7bUpgrade options
