QUEST 35B RL — Hardware Requirements & GPU Compatibility
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QUEST 35B RL is a 35.1B-parameter open language model from osunlp. It supports a context window of up to 262,144 tokens. At BF16 it needs about 70.60 GB of VRAM — see which GPUs and Macs can run it below.
403 downloads
4 likesSpecifications
- Publisher
- osunlp
- Parameters
- 35.1B
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-05-26
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does QUEST 35B RL Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 70.6 GB | 81.3 GB | 70.21 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run QUEST 35B RL?
BF16 · 70.6 GBQUEST 35B RL (BF16) requires 70.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 92+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 81.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run QUEST 35B RL?
BF16 · 70.6 GB5 devices with unified memory can run QUEST 35B RL, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Frequently Asked Questions
- How much VRAM does QUEST 35B RL need?
QUEST 35B RL requires 70.6 GB of VRAM at BF16. Full 262K context adds up to 10.7 GB (81.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 35.1B × 16 bits ÷ 8 = 70.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
BF1670.6 GBBF16 + full context81.3 GB- Can NVIDIA GeForce RTX 5090 run QUEST 35B RL?
No — QUEST 35B RL requires at least 70.6 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run QUEST 35B RL on a Mac?
QUEST 35B RL requires at least 70.6 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run QUEST 35B RL locally?
Yes — QUEST 35B RL can run locally on consumer hardware. At BF16 quantization it needs 70.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is QUEST 35B RL?
At BF16, QUEST 35B RL can reach ~41 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 70.6 × 0.55 = ~41 tok/s
Estimated speed at BF16 (70.6 GB)
~41 tok/s~31 tok/s~26 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of QUEST 35B RL?
At BF16, the download is about 70.21 GB.
- Which GPUs can run QUEST 35B RL?
No single consumer GPU has enough VRAM to run QUEST 35B RL at BF16 (70.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run QUEST 35B RL?
5 devices with unified memory can run QUEST 35B RL at BF16 (70.6 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.