OTel 2.0 LLM 31B IT QLoRA — Hardware Requirements & GPU Compatibility
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OTel 2.0 LLM 31B IT QLoRA is a 31.3B-parameter open language model from farbodtavakkoli. At BF16 it needs about 68.80 GB of VRAM — see which GPUs and Macs can run it below.
OTel 2.0 LLM 31B IT QLoRA runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · BF16 · 72.6 GB · ~8 tok/s
Macs from 96 GB (BF16 · ~8 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OTel 2.0 LLM 31B IT QLoRA
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT-QLoRA --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (23 GB headroom) · BF16
- Generation speed
- ~17 tok/s
- generation speed
- Cost per 1M output tokens
- $21.95
- per 1M output tokens
How Much VRAM Does OTel 2.0 LLM 31B IT QLoRA Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 68.8 GB | 76.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on H100 SXM 80 GB |
Selected BF16
Brain floating point 1672.6GBat 16K context
- Weights
- 68.0 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Server-class: ~72.6 GB at 16K context · one H100 SXM 80 GB
Which Hardware Can Run OTel 2.0 LLM 31B IT QLoRA?
BF16 · 68.8 GBOTel 2.0 LLM 31B IT QLoRA (BF16) requires 68.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 90+ GB is recommended. No consumer GPU has enough memory. 30 devices with unified memory can run OTel 2.0 LLM 31B IT QLoRA, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
96 GB
fits · 23.4 GB free · up to 32K ctx
3 GPUs · 2 Mac classes
RTX PRO 6000 Blackwell Max-Q Workstation Edition~17Simulate output on RTX PRO 6000 Blackwell Max-Q Workstation EditionRTX PRO 6000 Blackwell Workstation Edition~17Simulate output on RTX PRO 6000 Blackwell Workstation EditionRTX PRO 6000 Blackwell Server Edition~15Simulate output on RTX PRO 6000 Blackwell Server EditionM3 Ultra*96 GB~8Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~3Simulate output on MacBook Pro 14" M3 Max (96 GB)128–144 GB
fits · 55.4 GB free · up to 32K ctx
4 GPUs · 2 Mac classes · 4 device families
GH200 Grace Hopper Superchip~46Simulate output on GH200 Grace Hopper SuperchipH200 NVL~45Simulate output on H200 NVLH200 SXM~45Simulate output on H200 SXMM2 Ultra*128 GB~8Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~6Simulate output on Mac Studio M5 Max (128 GB)GB10~3Simulate output on ASUS Ascent GX10Jetson T5000 (Thor)~3Simulate output on NVIDIA Jetson AGX Thor Developer Kit
By VRAM tier · typical GPUs and Macs at BF16
Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 72.6 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Granite Switch 4.1 30BIBM | — | 22.7 GB | ~35 | same size |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
| Hy MT2 30B A3BTencent | — | 19.1 GB | ~170 | same size |
| Gemma 4 31BGoogle | — | 21.7 GB | ~32 | same size |
Frequently Asked Questions
How much VRAM does OTel 2.0 LLM 31B IT QLoRA need?
OTel 2.0 LLM 31B IT QLoRA requires 68.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 31.3B × 16 bits ÷ 8 = 62.5 GB
KV Cache + Overhead ≈ 6.3 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.
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run OTel 2.0 LLM 31B IT QLoRA?
No — OTel 2.0 LLM 31B IT QLoRA requires at least 68.8 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run OTel 2.0 LLM 31B IT QLoRA on a Mac?
Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run OTel 2.0 LLM 31B IT QLoRA. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 68.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run OTel 2.0 LLM 31B IT QLoRA locally?
Yes — OTel 2.0 LLM 31B IT QLoRA can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OTel 2.0 LLM 31B IT QLoRA?
At BF16, OTel 2.0 LLM 31B IT QLoRA can reach ~70 tok/s on AMD Instinct MI350X. 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 ÷ 68.8 × 0.65 = ~76 tok/s
Estimated speed at BF16 (68.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OTel 2.0 LLM 31B IT QLoRA?
At BF16, the download is about 62.55 GB.
Which GPUs can run OTel 2.0 LLM 31B IT QLoRA?
No single consumer GPU has enough VRAM to run OTel 2.0 LLM 31B IT QLoRA at BF16 (68.8 GB). Multi-GPU or professional hardware is required.
Which devices can run OTel 2.0 LLM 31B IT QLoRA?
30 devices with unified memory can run OTel 2.0 LLM 31B IT QLoRA at BF16 (68.8 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.