Published · Phase 1
Best GPU for Local LLM Inference
The best GPU for local LLMs is the fastest one that holds the complete chosen workload with headroom. For most users that means RTX 5090; choose RTX PRO 6000 for 70B/pro workloads and DGX Spark for >96 GB capacity or low-power large-model experimentation.
Editorial review: complete · Updated 2026-08-30
What this hardware is
What this hardware is guidance: Official hardware specs, model sizes, and adjacent public performance form the recommendation bands.
Who should consider it
Who should consider it guidance: 5090 suits individuals/small teams; Pro suits professional services and ECC/certification; Spark suits capacity-first labs and compact efficient systems.
Current market pricing
Current market pricing guidance: Do not buy memory you will never use or speed a required model cannot access. Total-system and operating cost matter more than GPU MSRP.
Memory / capacity
Memory / capacity guidance: Use 32 GB for 27B–35B 4 bit, 48 GB for 35B 8 bit or tight 70B 4 bit, 80–96 GB for robust 70B/120B low bit, and 128 GB for larger experiments and multiple services.
Observed model performance
Observed model performance guidance: Memory bandwidth determines speed after fit. RTX 5090/PRO generally outrun Spark on the same fitting model; Spark wins only when their smaller memory forces offload or a reduced model.
Software / runtime support
Software / runtime support guidance: All three tiers support modern CUDA runtimes, with Spark's ARM64 requiring more compatibility care. llama.cpp favors flexible local use; vLLM favors serving.
Strengths
Strengths guidance: A workload-first method produces a defensible recommendation instead of a universal winner.
Weaknesses
Weaknesses guidance: Model releases and runtime kernels evolve, so a hardware choice should retain flexibility and upgrade paths.
Alternatives
Alternatives guidance: Cloud GPUs avoid upfront cost for intermittent use; used previous-generation cards can offer strong value; multi-GPU systems need explicit topology/sharding plans.
Where to buy / check price
Where to buy / check price guidance: Do not buy memory you will never use or speed a required model cannot access. Total-system and operating cost matter more than GPU MSRP. Compare the exact product, memory configuration, warranty, seller, and complete-system cost before ordering.
Affiliate disclosure
Affiliate disclosure guidance: Any referral option is secondary to this recommendation: Define the model, precision, context, concurrency, and latency target first. Buy the lowest-cost tier with 10–20% measured headroom that meets all five. Merchant availability or commission never changes the technical ranking.
Evidence and limitations
- A benchmark champion can be the wrong purchase if the target model does not fit.
- Professional support, ECC, acoustics, and power can be hard requirements independent of tok/s.
Direct answer
Direct answer guidance: Define the model, precision, context, concurrency, and latency target first. Buy the lowest-cost tier with 10–20% measured headroom that meets all five.
Model / quant variants
Model / quant variants guidance: All three tiers support modern CUDA runtimes, with Spark's ARM64 requiring more compatibility care. llama.cpp favors flexible local use; vLLM favors serving.
Approximate weight footprint
Approximate weight footprint guidance: Use 32 GB for 27B–35B 4 bit, 48 GB for 35B 8 bit or tight 70B 4 bit, 80–96 GB for robust 70B/120B low bit, and 128 GB for larger experiments and multiple services.
Hardware tiers
Hardware tiers guidance: Use 32 GB for 27B–35B 4 bit, 48 GB for 35B 8 bit or tight 70B 4 bit, 80–96 GB for robust 70B/120B low bit, and 128 GB for larger experiments and multiple services. Cloud GPUs avoid upfront cost for intermittent use; used previous-generation cards can offer strong value; multi-GPU systems need explicit topology/sharding plans.
Observed configurations
Observed configurations guidance: Memory bandwidth determines speed after fit. RTX 5090/PRO generally outrun Spark on the same fitting model; Spark wins only when their smaller memory forces offload or a reduced model.
Context / concurrency effects
Context / concurrency effects guidance: Long context and concurrency often move a buyer up one memory tier. Include them before comparing products.
Performance expectations
Performance expectations guidance: Memory bandwidth determines speed after fit. RTX 5090/PRO generally outrun Spark on the same fitting model; Spark wins only when their smaller memory forces offload or a reduced model.
Best-value configurations
Best-value configurations guidance: Do not buy memory you will never use or speed a required model cannot access. Total-system and operating cost matter more than GPU MSRP. Define the model, precision, context, concurrency, and latency target first. Buy the lowest-cost tier with 10–20% measured headroom that meets all five.
What will not fit / weak evidence
- A benchmark champion can be the wrong purchase if the target model does not fit.
- Professional support, ECC, acoustics, and power can be hard requirements independent of tok/s.
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.