Agents-A1 4B needs ~4.5 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Operating mode
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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.3 GB host RAM)
Decode
21.5 tok/s
TTFT
8998 ms
Safe context
4K
Memory
4.5 GB / 4.0 GB
Offload
10%
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.
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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~0.2 GB host RAM) | 24.2 tok/s | 4368 ms | 4K |
| Coding | B | Very compromised (needs ~0.3 GB host RAM) | 21.5 tok/s | 8998 ms | 4K |
| Agentic Coding | F | Too heavy | 17.3 tok/s | 16232 ms | 4K |
| Reasoning | B | Very compromised (needs ~0.3 GB host RAM) | 21.5 tok/s | 10634 ms | 4K |
| RAG | F | Too heavy | 17.3 tok/s | 20290 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Agents-A1 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~86 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 | 85.5 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 72.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 72.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 63.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 63.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 63.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 63.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 63.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 63.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 63.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 63.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 63.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 63.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 63.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 63.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 57.4 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Agents-A1 4B (4.5B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q1_0_G128 | 1.125 | 0.6 GB | Very Low | A82 |
Q2_0_G128 | 1.71 | 1.2 GB | Low | A81 |
Q2_KBest for your GPU | 2 | 1.8 GB | Low | A81 |
Q3_K_S | 3 | 2.2 GB | Low | F0 |
NVFP4 | 4 | 2.5 GB | Medium | F0 |
Q4_K_M | 4 | 2.7 GB | Medium | F0 |
Q5_K_M | 5 | 3.2 GB | High | F0 |
Q6_K | 6 | 3.7 GB | High | F0 |
Q8_0 | 8 | 4.8 GB | Very High | F0 |
F16 | 16 | 9.2 GB | Maximum | F0 |
Copy-paste commands to run Agents-A1 4B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternScience/Agents-A1-4B" \
--hf-file "Agents-A1-4B-Q4_K_M.gguf" \
-c 4096 -ngl 99Opções de upgrade
GTX 1660 Super 6GBOpção econômicaRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 155%.
~$229 MSRP
GTX 1060 6GBMelhor custo-benefícioRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 56%.
~$249 MSRP
RTX 3050 8GBUpgrade NVIDIARemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 72%.
~$249 MSRP
Yes, RTX 3050 Ti Laptop 4GB can run Agents-A1 4B with a B grade (Very compromised (needs ~0.3 GB host RAM)). Expected decode speed: 21.5 tok/s.
Agents-A1 4B (4.5B parameters) requires approximately 4.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Agents-A1 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, Agents-A1 4B achieves approximately 21.5 tokens per second decode speed with a time-to-first-token of 8998ms using Q4_K_M quantization.
For coding workloads, Agents-A1 4B on RTX 3050 Ti Laptop 4GB receives a B grade with 21.5 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, Agents-A1 4B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/agents-a1-4b-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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