Can Agents-A1 35B A3B run on RTX 3090 24GB?

Q: What should I upgrade first if Agents-A1 35B A3B feels slow on RTX 3090 24GB?

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

A77Great

Estimated from fit model

Agents-A1 35B A3B needs ~25.9 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 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

Q4_K_M (Medium quality) — 25.0 GB, 62.1 tok/s, Runs with offload (needs ~0.9 GB host RAM)

25.0 GB required24.0 GB available

104% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.9 GB host RAM)

Decode

62.1 tok/s

TTFT

3118 ms

Safe context

Memory

25.0 GB / 24.0 GB

Memory breakdown

Weights21.4 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsAgents-A1 35B A3B on RTX 3090 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: 62.1 tok/s decode · 3.1s TTFT (warm) · 155 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 {ram} 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	A	Runs with offload	55.7 tok/s	1897 ms	4K
Coding	A	Very compromised	52.9 tok/s	3657 ms	4K
Agentic Coding	A	Very compromised	48.0 tok/s	5861 ms	4K
Reasoning	A	Very compromised	52.9 tok/s	4322 ms	4K
RAG	A	Very compromised	48.0 tok/s	7326 ms	4K

Inference speed

Agents-A1 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Agents-A1 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	139.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	77.7	Offloads

Quantization options

How Agents-A1 35B A3B (35.099998474121094B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	5.1 GB	Very Low	A81
Q2_0_G128	1.71	9.4 GB	Low	A84
Q2_K	2

Get started

Copy-paste commands to run Agents-A1 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "InternScience/Agents-A1" \
  --hf-file "Agents-A1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 3090 24GB run Agents-A1 35B A3B?

Yes, RTX 3090 24GB can run Agents-A1 35B A3B with a A grade (Very compromised). Expected decode speed: 52.9 tok/s.

How much VRAM does Agents-A1 35B A3B need?

Agents-A1 35B A3B (35.099998474121094B parameters) requires approximately 25.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Agents-A1 35B A3B?

The recommended quantization for Agents-A1 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Agents-A1 35B A3B run at on RTX 3090 24GB?

On RTX 3090 24GB, Agents-A1 35B A3B achieves approximately 52.9 tokens per second decode speed with a time-to-first-token of 3657ms using Q4_K_M quantization.

Can RTX 3090 24GB run Agents-A1 35B A3B for coding?

For coding workloads, Agents-A1 35B A3B on RTX 3090 24GB receives a A grade with 52.9 tok/s and 4K context.

What context window can Agents-A1 35B A3B use on RTX 3090 24GB?

On RTX 3090 24GB, Agents-A1 35B A3B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Agents-A1 35B A3B feels slow on RTX 3090 24GB?

See all results for RTX 3090 24GB See all hardware for Agents-A1 35B A3B

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