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URL: https://willitrunai.com/can-run/granite-code-20b-on-tesla-p100-16gb

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Can Granite Code 20B run on Tesla P100 16GB?

BARELY — Tight on Memory

B69Good
○Estimated from fit model

Granite Code 20B needs ~17.9 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

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

Q4_K_M (Medium quality) — 17.9 GB, 21.9 tok/s, Very compromised (needs ~1.3 GB host RAM)
17.9 GB required16.0 GB available
112% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

21.9 tok/s

TTFT

8854 ms

Safe context

7K

Memory

17.9 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB
KV Cache3.2 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite Code 20B on Tesla P100 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: 21.9 tok/s decode · 8.9s TTFT (warm) · 55 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload (needs ~0.2 GB host RAM)26.8 tok/s3937 ms7K
CodingBVery compromised20.2 tok/s9562 ms7K
Agentic CodingFToo heavy15.3 tok/s18439 ms7K
ReasoningBVery compromised (needs ~1.3 GB host RAM)21.9 tok/s10464 ms7K
RAGFToo heavy15.3 tok/s23049 ms

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowA81
Q3_K_S
3
9.8 GB
LowA81
NVFP4
4

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Upgrade options

Hardware that runs Granite Code 20B well

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick
20 GB VRAM (+4)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.24.9 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBest value
24 GB VRAM (+8)936 GB/s (+204)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.50.4 tok/s decode

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

Raises estimated decode speed by about 130%.

~$1,499 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade
24 GB VRAM (+8)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.50 tok/s decode

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

Raises estimated decode speed by about 128%.

~$1,599 MSRP

Frequently asked questions

See all results for Tesla P100 16GBSee all hardware for Granite Code 20B
7K
11.2 GB
Medium
A80
Q4_K_MBest for your GPU
4
12.2 GB
MediumA80
Q5_K_M
5
14.4 GB
HighF0
Q6_K
6
16.4 GB
HighF0
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

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.