AI inference costs dropped up to 10x on Nvidia's Blackwell — but hardware is only half the equation
Source: VentureBeat
Lowering the Cost of Inference
A new analysis released Thursday by Nvidia shows that four leading inference providers are achieving 4×–10× reductions in cost per token. The savings come from a combination of hardware, software, and model choices.
Providers & Use‑Cases
| Provider | Primary Use‑Case | Reported Cost Reduction |
|---|---|---|
| Baseten | Healthcare | 4×–6× |
| DeepInfra | Gaming | 5× |
| Fireworks AI | Agentic chat | 7× |
| Together AI | Customer service | 8×–10× |
All deployments use Nvidia’s Blackwell platform together with open‑source models.
How the Reductions Were Achieved
| Element | Contribution | Details |
|---|---|---|
| Blackwell hardware | ~2× gain | Pure hardware improvements delivered up to a 2× boost in throughput for some workloads. |
| Optimized software stacks | Additional 2×–3× gain | Low‑precision formats (e.g., NVFP4) and tuned inference pipelines squeeze extra performance. |
| Open‑source models | 4×–10× overall | Switching from proprietary APIs to open‑source models that match frontier‑level intelligence eliminates premium API fees and enables deeper hardware‑software co‑design. |
“Performance is what drives down the cost of inference. What we’re seeing in inference is that throughput literally translates into real dollar value and driving down the cost.”
— Dion Harris, Senior Director of HPC and AI Hyperscaler Solutions, Nvidia (VentureBeat exclusive)
Key Takeaways
- Higher‑performance infrastructure lowers per‑token cost – investing in faster GPUs and optimized pipelines pays off through higher throughput.
- Open‑source models are now competitive with proprietary alternatives, offering both cost and flexibility benefits.
- Low‑precision formats such as NVFP4 are essential for extracting the maximum efficiency from the hardware.
Enterprises scaling AI from pilots to millions of users can therefore achieve significant cost savings by combining Nvidia’s Blackwell hardware with modern software stacks and open‑source models.
Production Deployments Show 4×–10× Cost Reductions
Nvidia’s recent blog post highlights four customer deployments that combine Blackwell infrastructure, optimized software stacks, and open‑source models to slash inference costs across a range of industry workloads. Below is a concise, markdown‑formatted summary of each case study.
| Customer | Workload | Cost Reduction | Key Performance Gains | Stack Used |
|---|---|---|---|---|
| Sully.ai | Healthcare AI (medical coding & note‑taking) | 10× (‑90 % cost) | • Response time ↓ 65 % | |
| • >30 M minutes of physician time returned | Open‑source models on Baseten’s Blackwell‑powered platform | |||
| Latitude | Gaming inference for AI‑Dungeon | 4× overall (2× from hardware, 2× from precision) | • Cost per M tokens: $0.20 → $0.10 → $0.05 | |
| • Native NVFP4 low‑precision format enabled the final 2× gain | Large MoE models on DeepInfra’s Blackwell deployment | |||
| Sentient Foundation | Agentic chat platform (multi‑agent workflows) | 1.25×–2× (25 %–50 % better cost efficiency) | • Processed 5.6 M queries in one week during viral launch | |
| • Maintained low latency throughout | Fireworks AI’s Blackwell‑optimized inference stack | |||
| Decagon | AI‑powered voice customer support | 6× cost reduction per query | • Response time “Enterprises need to work back from their workloads and use case and cost constraints,” |
— Shruti Koparkar, AI Product Marketing, Nvidia, told VentureBeat.
If your workloads do not involve high‑volume, latency‑sensitive applications (e.g., millions of requests per month with sub‑second latency budgets), consider first:
- Software‑level optimizations
- Model switching or quantization
before committing to new hardware.
2. Test, Don’t Rely Solely on Provider Specs
Providers publish throughput and latency numbers, but those reflect ideal conditions.
“If it’s a highly latency‑sensitive workload, they might want to test a couple of providers and see who meets the minimum they need while keeping the cost down,”
— Shruti Koparkar.
Action: Run your actual production workload on multiple Blackwell providers (or alternatives) to measure:
- Real‑world latency under typical traffic spikes
- Throughput at your target batch sizes
- Cost per token in your usage pattern
3. Follow a Staged Evaluation Approach
Latitude’s model provides a useful template:
| Stage | What Was Done | Result |
|---|---|---|
| 1️⃣ | Migrate to Blackwell hardware | ~2× performance improvement |
| 2️⃣ | Adopt NVFP4 precision format | ~4× total cost reduction |
Takeaways for teams on Hopper or other hardware:
- Precision‑format changes (e.g., FP8, INT4) can yield sizable gains on existing GPUs.
- Software optimizations (TensorRT‑LLM, vLLM, Dynamo) may capture a portion of the potential savings without new hardware.
- Open‑source models can be tested on current infrastructure to gauge how much of the advertised reduction is achievable today.
4. Compare Software Stacks Across Providers
Even when multiple vendors offer Blackwell GPUs, their software stacks differ:
- Nvidia integrated stack – Dynamo + TensorRT‑LLM
- Third‑party stacks – vLLM, custom inference runtimes
“Performance deltas exist between these configurations,”
— Harris (industry analyst).
Action: Identify which stack each provider uses and benchmark it against your workload’s characteristics.
5. Evaluate the Full Economic Equation
Beyond cost per token, consider:
| Factor | Example Providers |
|---|---|
| Optimized inference services | Baseten, DeepInfra, Fireworks, Together |
| Managed cloud services | AWS, Azure, Google Cloud |
| Operational overhead | Vendor management, SLA handling, monitoring |
| Complexity vs. cost trade‑off | Higher per‑token cost may be justified by lower ops burden |
Decision tip: Compute total cost of ownership (TCO), including:
- Inference pricing
- Engineering time for integration & maintenance
- SLA and support costs
Choose the approach that delivers the best economics for your specific situation.
Quick Checklist for Teams
- Quantify workload volume & latency budget
- Run baseline benchmarks on current hardware
- Test multiple providers (including non‑Blackwell options) with real traffic patterns
- Evaluate precision formats (NVFP4, FP8, INT4) on existing GPUs
- Compare software stacks (TensorRT‑LLM, vLLM, Dynamo)
- Calculate TCO (inference cost + operational overhead)
By following this systematic, data‑driven approach, teams can determine whether a full Blackwell migration is warranted—or if software and precision optimizations on existing infrastructure will meet their performance and cost goals.