Tri 21B Think — Hardware Requirements & GPU Compatibility
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Tri 21B Think is a 20.7B-parameter open language model from trillionlabs. It supports a context window of up to 32,768 tokens. At BF16 it needs about 42.17 GB of VRAM — see which GPUs and Macs can run it below.
5.6K downloads
27 likesSpecifications
- Publisher
- trillionlabs
- Parameters
- 20.7B
- Architecture
- TrillionForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 124,416
- Release Date
- 2026-02-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Tri 21B Think Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 42.2 GB | 48.5 GB | 41.45 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Tri 21B Think?
BF16 · 42.2 GBTri 21B Think (BF16) requires 42.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 55+ GB is recommended. Using the full 33K context window can add up to 6.3 GB, bringing total usage to 48.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Tri 21B Think?
BF16 · 42.2 GB11 devices with unified memory can run Tri 21B Think, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Tri 21B Think need?
Tri 21B Think requires 42.2 GB of VRAM at BF16. Full 33K context adds up to 6.3 GB (48.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20.7B × 16 bits ÷ 8 = 41.5 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 33K context)
VRAM usage by quantization
BF1642.2 GBBF16 + full context48.5 GB- Can NVIDIA GeForce RTX 5090 run Tri 21B Think?
No — Tri 21B Think requires at least 42.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Tri 21B Think on a Mac?
Tri 21B Think requires at least 42.2 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 Tri 21B Think locally?
Yes — Tri 21B Think can run locally on consumer hardware. At BF16 quantization it needs 42.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Tri 21B Think?
At BF16, Tri 21B Think can reach ~69 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 ÷ 42.2 × 0.55 = ~69 tok/s
Estimated speed at BF16 (42.2 GB)
~69 tok/s~52 tok/s~43 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Tri 21B Think?
At BF16, the download is about 41.45 GB.
- Which GPUs can run Tri 21B Think?
No single consumer GPU has enough VRAM to run Tri 21B Think at BF16 (42.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Tri 21B Think?
11 devices with unified memory can run Tri 21B Think at BF16 (42.2 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.