DBRX Instruct

When to pick this model

• Databricks-native pipelines that need an in-house model
• Code and math workloads where 36B active params shine
• Research comparisons against modern frontier MoEs
• Multi-GPU deployments already provisioned for 100B+ models
• Internal evals before migrating to DeepSeek V3 or R1

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, DBRX Instruct is server-class even at Q4_K_M (76 GB). Stepping up to Q8_0 nearly doubles the footprint to 140 GB, and unquantized FP16 weights take 264 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, DBRX Instruct needs roughly 112 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 2 tokens/sec on entry-level GPUs, on the order of 8 tokens/sec on a mid-range card, and up to 22 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches DBRX Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run DBRX Instruct?

To run DBRX Instruct locally at Q4, you need ~76 GB for Q4 weights alone. Hardware option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put DBRX Instruct in context: its MMLU score of 73.7 ranks #17 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 70.1 ranks #20 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• State-of-the-art quality at March 2024 release
• Strong on code and math benchmarks
• Databricks Open Model License is broadly permissive
• 12T tokens of high-quality training data

Limitations

• ~76 GB VRAM at Q4 demands multi-GPU serving
• Largely outclassed by DeepSeek V3 and R1 in 2025
• HuggingFace repo is gated, slowing access

Typical workloads

In our catalog grid, DBRX Instruct is filed under Enterprise MoE, Pro Chat — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.

The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Databricks Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: MoE · 132B total / 36B active · 16 experts, 4 active per token

Training: Databricks — 12T high-quality tokens, strong in code and science.

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

ollama run dbrx

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does DBRX Instruct need?

At the recommended Q4_K_M quantization, DBRX Instruct needs about 76 GB of VRAM. Q8_0 takes 140 GB, and unquantized FP16 weights take 264 GB.

Can DBRX Instruct run without a GPU?

Yes — with roughly 112 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does DBRX Instruct support?

DBRX Instruct supports a 32k-token context window (32,768 tokens).

Can I use DBRX Instruct commercially?

DBRX Instruct ships under the Databricks Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is DBRX Instruct on consumer hardware?

Our compatibility engine estimates on the order of 8 tokens/sec on a mid-range GPU and up to 22 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of DBRX Instruct should I download first?

Start with Q4_K_M (76 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.