willitrun·ai

Can OpenHermes 2.5 7B run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

B56Good
Estimated from fit model

OpenHermes 2.5 7B needs ~8.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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) — 8.6 GB, 61.8 tok/s, Runs well
8.6 GB required12.0 GB available
72% VRAM used

Fit status

Runs well

Decode

61.8 tok/s

TTFT

3135 ms

Safe context

8K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsOpenHermes 2.5 7B on RTX 3500 Ada Laptop 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 61.8 tok/s decode · 3.1s TTFT (warm) · 154 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well61.8 tok/s1710 ms8K
CodingBRuns well61.8 tok/s3135 ms8K
Agentic CodingCTight fit61.8 tok/s4560 ms8K
ReasoningBRuns well61.8 tok/s3705 ms8K
RAGCTight fit61.8 tok/s5700 ms8K

Inference speed

OpenHermes 2.5 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for OpenHermes 2.5 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M98.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M98.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M98.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M98.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M98.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M98.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M98.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M98.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M95.2Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M94.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M94.4Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M60.4Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M59.8Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M55.4Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M48.7Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M46.0Offloads

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.

Quantization options

How OpenHermes 2.5 7B (7B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC50
Q3_K_S
3
3.4 GB
LowC51
NVFP4
4
3.9 GB
MediumC51
Q4_K_M
4
4.3 GB
MediumC52
Q5_K_M
5
5.0 GB
HighC53
Q6_K
6
5.7 GB
HighC53
Q8_0Best for your GPU
8
7.5 GB
Very HighC53
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run OpenHermes 2.5 7B on your machine.

Run

ollama run openhermes

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run OpenHermes 2.5 7B?

Yes, RTX 3500 Ada Laptop 12GB can run OpenHermes 2.5 7B with a B grade (Runs well). Expected decode speed: 61.8 tok/s.

How much VRAM does OpenHermes 2.5 7B need?

OpenHermes 2.5 7B (7B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for OpenHermes 2.5 7B?

The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will OpenHermes 2.5 7B run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, OpenHermes 2.5 7B achieves approximately 61.8 tokens per second decode speed with a time-to-first-token of 3135ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run OpenHermes 2.5 7B for coding?

For coding workloads, OpenHermes 2.5 7B on RTX 3500 Ada Laptop 12GB receives a B grade with 61.8 tok/s and 8K context.

What context window can OpenHermes 2.5 7B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, OpenHermes 2.5 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GBSee all hardware for OpenHermes 2.5 7B
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