Published · Phase 3

Qwen3.5 35B Hardware Requirements

Qwen3.5-35B-A3B needs 24–32 GB for a practical 4-bit deployment, 48 GB for comfortable 8 bit, and around 80 GB for BF16. The RTX 5090 is the recommended single-user 4-bit platform; higher-memory systems target precision and concurrency.

Editorial review: complete · Updated 2026-08-30

Direct answer

Direct answer guidance: Buy 32 GB for one 4-bit service, 48 GB for 8 bit/longer context, and 96–128 GB for high-precision research or multiple resident models.

Model / quant variants

Model / quant variants guidance: Use vLLM for serving and current MoE kernels; use llama.cpp for a trusted GGUF when local simplicity is the priority.

Approximate weight footprint

Approximate weight footprint guidance: The official repository is about 72 GB. Budget 20–24 GB at 4 bit, 38–45 GB at 8 bit, and 75–82 GB at BF16 before cache and runtime workspaces.

Hardware tiers

Hardware tiers guidance: The official repository is about 72 GB. Budget 20–24 GB at 4 bit, 38–45 GB at 8 bit, and 75–82 GB at BF16 before cache and runtime workspaces. Qwen3.6 is the newer coding choice; Qwen3.8 is dense and simpler; Mistral Small 4 offers a larger MoE on high-memory hardware.

Observed configurations

Observed configurations guidance: A tuned 4-bit 5090 deployment can land around 80–160 tok/s, while a capacity-first Spark will be slower but can hold higher precision and much larger caches.

Context / concurrency effects

Context / concurrency effects guidance: Use 8K–16K on 32 GB and 32K on 48 GB after profiling. Larger advertised windows remain specialist configurations.

Performance expectations

Performance expectations guidance: A tuned 4-bit 5090 deployment can land around 80–160 tok/s, while a capacity-first Spark will be slower but can hold higher precision and much larger caches.

Best-value configurations

Best-value configurations guidance: The 4-bit consumer tier is the best value. Higher precision should earn its hardware premium in a controlled task evaluation. Buy 32 GB for one 4-bit service, 48 GB for 8 bit/longer context, and 96–128 GB for high-precision research or multiple resident models.

What will not fit / weak evidence

  • Reserve memory for the vision tower when using multimodal inputs.
  • Community quantization quality and metadata vary; prefer traceable conversions.

Planner CTA

Compare this recommendation against your model, context, concurrency, latency, and budget in the ComputeSage Planner.

Sources / methodology

Recommendations combine official specifications, public model/runtime documentation, adjacent benchmark observations, and clearly labeled engineering estimates. Review the ComputeSage evidence methodology.