MacBook Pro M4 Max 36GBOpción económica
36 GB Unified (+12)
B
Este setup está bastante equilibrado para este modelo.71.6 tok/s decodificación
~$2,499 MSRP
Can DeepSeek R1 Distill 7B run on RTX 4500 Ada 24GB?
YES — Runs Great
B66Good
Estimated from fit model
DeepSeek R1 Distill 7B needs ~8.7 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~80 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 8.7 GB, 86.8 tok/s, Runs well
8.7 GB required24.0 GB available
36% VRAM used
Fit status
Runs well
Decode
86.8 tok/s
TTFT
2231 ms
Safe context
33K
Memory
8.7 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill 7B on RTX 4500 Ada 24GB
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: 86.8 tok/s decode · 2.2s TTFT (warm) · 217 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 | 79.9 tok/s | 1321 ms | 33K |
| Coding | B | Runs well | 79.9 tok/s | 2422 ms | 33K |
| Agentic Coding | B | Runs well | 79.9 tok/s | 3523 ms | 33K |
| Reasoning | B | Runs well | 79.9 tok/s | 2863 ms | 33K |
| RAG | B | Runs well | 79.9 tok/s | 4404 ms | 33K |
Quantization options
How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B61 |
Q3_K_S | 3 | 3.4 GB | Low | B62 |
NVFP4 | 4 | 3.9 GB | Medium | B62 |
Q4_K_M | 4 | 4.3 GB | Medium | B62 |
Q5_K_M | 5 | 5.0 GB | High | B62 |
Q6_K | 6 | 5.7 GB | High | B63 |
Q8_0 | 8 | 7.5 GB | Very High | B64 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B67 |
Get started
Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.
Run
ollama run deepseek-r1:7bOpciones de mejora
Hardware que ejecuta bien DeepSeek R1 Distill 7B
Frequently asked questions
Can RTX 4500 Ada 24GB run DeepSeek R1 Distill 7B?
Yes, RTX 4500 Ada 24GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 79.9 tok/s.
How much VRAM does DeepSeek R1 Distill 7B need?
DeepSeek R1 Distill 7B (7B parameters) requires approximately 8.7 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 4500 Ada 24GB?
On RTX 4500 Ada 24GB, DeepSeek R1 Distill 7B achieves approximately 79.9 tokens per second decode speed with a time-to-first-token of 2422ms using Q4_K_M quantization.
Can RTX 4500 Ada 24GB run DeepSeek R1 Distill 7B for coding?
For coding workloads, DeepSeek R1 Distill 7B on RTX 4500 Ada 24GB receives a B grade with 79.9 tok/s and 33K context.
What context window can DeepSeek R1 Distill 7B use on RTX 4500 Ada 24GB?
On RTX 4500 Ada 24GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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