48 GB VRAM (+8)
B
This setup is broadly balanced for this model.41.3 tok/s decode
~$4,650 MSRP
Can InternLM 20B run on NVIDIA A100 40GB?
YES — Tight Fit
B62Good
○Estimated from fit model
InternLM 20B needs ~37.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q5_K_M quantization, expect ~93 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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
Q5_K_M (High quality) — 37.6 GB, 92.5 tok/s, Tight fit
37.6 GB required40.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
92.5 tok/s
TTFT
2092 ms
Safe context
8K
Memory
37.6 GB / 40.0 GB
Memory breakdown
Weights14.4 GB
KV Cache18.3 GB
Runtime0.9 GB
Headroom4.0 GB
See how fast it feels
See how fast it feelsInternLM 20B on NVIDIA A100 40GB
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: 92.5 tok/s decode · 2.1s TTFT (warm) · 231 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 92.5 tok/s | 1141 ms | 8K |
| Coding | B | Tight fit | 92.5 tok/s | 2092 ms | 8K |
| Agentic Coding | F | Too heavy | 34.3 tok/s | 8216 ms | 8K |
| Reasoning | B | Tight fit | 92.5 tok/s | 2473 ms | 8K |
| RAG | F | Too heavy | 34.3 tok/s | 10269 ms | 8K |
Quantization options
How InternLM 20B (20B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | C52 |
NVFP4 | 4 |
Get started
Copy-paste commands to run InternLM 20B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "internlm/internlm2_5-20b-chat" \
--hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options