OTel LLM 27B IT — Hardware Requirements & GPU Compatibility
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OTel LLM 27B IT is a 27B-parameter open language model from farbodtavakkoli. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 17.87 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 16 GB (Q2_K · 16K)Best on 24 GB (Q4_K_M · ~37 tok/s · 32K)Macs from 24 GB (Q4_K_M · ~12 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OTel LLM 27B IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve farbodtavakkoli/OTel-LLM-27B-IT --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~34 tok/s
- generation speed
- Cost per 1M output tokens
- $1.38
- per 1M output tokens
How Much VRAM Does OTel LLM 27B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 13.1 GB | 16.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~50 tok/s | |
| 14.8 GB | 18.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~44 tok/s | |
| 17.9 GB | 21.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~37 tok/s | |
| 20.9 GB | 24.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~31 tok/s | |
| 23.9 GB | 27.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~27 tok/s | |
| 28.7 GB | 32.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 30.1 GB | |
| 55.7 GB | 59.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 57.1 GB |
Selected Q4_K_M
4-bit medium quantization19.3GBat 16K context
- Weights
- 16.2 GB
- KV cache
- 2.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 4.7 GB free · ~37 tok/s
≥50 tok/s needs ≥1.37 TB/s · RTX 5090 ~65
Which Hardware Can Run OTel LLM 27B IT?
Q4_K_M · 17.9 GBOTel LLM 27B IT (Q4_K_M) requires 17.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 131K context window can add up to 17.5 GB, bringing total usage to 35.4 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run OTel LLM 27B IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 0.7 GB free · up to 16K ctx
1 GPU
RX 7900 XT~27Simulate output on RX 7900 XT32–40 GB
fits · 12.7 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~65Simulate output on RTX 5090M5 Max36 GB~18Simulate output on Mac Studio M5 Max (36 GB)M532 GB~6Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | same size |
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | same size |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | same size |
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | same size |
| Gemma 2 27B ITGoogle | 72.7 | 19.5 GB | ~37 | same size |
| Gemma 4 26B A4BGoogle | — | 16.5 GB | ~171 | same size |
Specifications
- Publisher
- farbodtavakkoli
- Parameters
- 27B
- Architecture
- Gemma3ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,208
- Release Date
- 2026-02-11
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does OTel LLM 27B IT need?
OTel LLM 27B IT requires 17.9 GB of VRAM at Q4_K_M, or 55.7 GB at BF16. Full 131K context adds up to 17.5 GB (35.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 19.2 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run OTel LLM 27B IT?
Yes, at Q6_K (23.9 GB) or lower. Higher quantizations like Q8_0 (28.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for OTel LLM 27B IT?
For OTel LLM 27B IT, Q4_K_M (17.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (20.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 13.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run OTel LLM 27B IT on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run OTel LLM 27B IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 17.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run OTel LLM 27B IT locally?
Yes — OTel LLM 27B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OTel LLM 27B IT?
At Q4_K_M, OTel LLM 27B IT can reach ~269 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 17.9 × 0.65 = ~291 tok/s
Estimated speed at Q4_K_M (17.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OTel LLM 27B IT?
At Q4_K_M, the download is about 16.20 GB. The full-precision BF16 version is 54.00 GB. The smallest option (Q2_K) is 11.47 GB.
Which GPUs can run OTel LLM 27B IT?
8 consumer GPUs can run OTel LLM 27B IT at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run OTel LLM 27B IT?
122 devices with unified memory can run OTel LLM 27B IT at Q4_K_M (17.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.