Can InternLM 20B run on NVIDIA V100 32GB?

Q: What should I upgrade first if InternLM 20B feels slow on NVIDIA V100 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

C48Usable

Estimated from fit model

InternLM 20B needs ~36.8 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q5_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 36.8 GB, 28.9 tok/s, Very compromised (needs ~1.9 GB host RAM)

36.8 GB required32.0 GB available

115% VRAM needed

4.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

28.9 tok/s

TTFT

6696 ms

Safe context

Memory

36.8 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsInternLM 20B on NVIDIA V100 32GB
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: 28.9 tok/s decode · 6.7s TTFT (warm) · 72 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	42.7 tok/s	2472 ms	8K
Coding	C	Very compromised (needs ~1.9 GB host RAM)	28.9 tok/s	6696 ms	8K
Agentic Coding	F	Too heavy	14.9 tok/s	18960 ms	8K
Reasoning	C	Very compromised (needs ~1.9 GB host RAM)	28.9 tok/s	7913 ms	8K
RAG	F	Too heavy	14.9 tok/s	23700 ms

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C53
Q3_K_S	3	9.8 GB	Low	C54
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 99

Upgrade options

Hardware that runs InternLM 20B well

RTX A6000 48GBBudget pick

48 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.41.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 43%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+444)

Removes host-memory offload, which is usually the single biggest latency and throughput win.80 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 177%.

~$4,999 MSRP

NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.38.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 34%.

~$5,500 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run InternLM 20B?

Yes, NVIDIA V100 32GB can run InternLM 20B with a C grade (Very compromised (needs ~1.9 GB host RAM)). Expected decode speed: 28.9 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 36.8 GB of memory with Q5_K_M quantization.

What is the best quantization for InternLM 20B?

The recommended quantization for InternLM 20B is Q5_K_M, which balances quality and memory efficiency.

What speed will InternLM 20B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, InternLM 20B achieves approximately 28.9 tokens per second decode speed with a time-to-first-token of 6696ms using Q5_K_M quantization.

Can NVIDIA V100 32GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA V100 32GB receives a C grade with 28.9 tok/s and 8K context.

What context window can InternLM 20B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, InternLM 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if InternLM 20B feels slow on NVIDIA V100 32GB?

See all results for NVIDIA V100 32GB See all hardware for InternLM 20B

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