Model Optimization & Quantization | TensorWard
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TensorWard Optimize

Make frontier models fit the hardware you already own.

Hardware-aware model selection, quantization, compression, and quality validation so large models run within your VRAM, latency, and quality envelope — without guessing.

THE PROBLEM

You have GPUs, a target workload, and a model that is either too large, too slow, or unvalidated after compression.

Off-the-shelf recipes rarely match your memory budget, quality bar, or deployment constraints.

TYPICAL ENGAGEMENT

Optimize work often starts inside an Audit or Pilot. Standalone optimization engagements are available when model fit is the primary blocker.

AUDIT$7,500 fixed · 1–2 weeks
PILOTfrom $25,000 · 4–8 weeks
FIG. 01 — WHERE OPTIMIZE SITS
STAGE 01
FRONTIER MODEL
Open-weight · License-aware · Workload fit
STAGE 02 · YOU ARE HERE
OPTIMIZE
Quantize · Validate · Fit
STAGE 03
RUNTIME
Serve · Scale · Observe
STAGE 04
AGENTS
Integrate · Automate · Operate
WHAT WE ACTUALLY DO

Technical capabilities

Hardware-aware model selection
Model quantization (GGUF, AWQ/GPTQ-style where appropriate)
Extreme / custom quantization strategies
FP8 / NVFP4 / low-bit deployment strategy where hardware supports it
Model compression & conversion
LoRA / QLoRA strategy and fine-tuning guidance
Context and memory optimization
Quality validation after compression
Benchmarking against real workloads
Hardware-specific deployment recipes
Rapid evaluation of newly released models
WHAT YOU RECEIVE

Deliverables

→Model shortlist with fit rationale
→Quantization / compression plan
→Benchmark report (memory, latency, throughput, quality)
→Deployment recipe for target hardware
→Tradeoff documentation and recommendations
HOW SUCCESS IS MEASURED

Acceptance criteria

Targets are set with you at kickoff and measured on your hardware. Values below are the shape of the report, not published results.

METRIC
TARGET
MEASURED
Peak VRAM
set at kickoff
—
Tokens / sec (gen)
set at kickoff
—
Time to first token
set at kickoff
—
Task quality vs. baseline
set at kickoff
—
WHO IT IS FOR
Teams with GPUs but unclear model fit
Organizations that need quality validated after quantization
Platform teams sizing VRAM for private inference
Engineers comparing open-weight candidates for a workload
FAQ
Do you only work with open-weight models?+

We specialize in open and frontier models that can run on infrastructure you control. Model choice follows workload, license, and hardware — not a preferred vendor.

Will quantization destroy quality?+

Not if it is measured. We define evaluation criteria for your task, compress against those criteria, and document quality vs. resource tradeoffs so you can decide with data.

Can you evaluate a model that just released?+

Yes. Rapid evaluation of newly released models against your hardware and workload is a core Optimize capability.

Ready to talk about Optimize?