Key Takeaways

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• 501 t/s is a software achievement. Inference optimization at the kernel and serving layer can close a substantial portion of the gap with specialized ASIC hardware without the procurement cost or vendor lock-in.
• TTFT and throughput serve different use cases. Optimize throughput for agentic pipelines and batch workloads. Optimize TTFT for interactive, real-time, or voice applications.
• The right provider depends on where you actually are. Startups need self-serve APIs with transparent pricing. Enterprise teams running at scale need SLA-backed, dedicated infrastructure. Compliance-sensitive verticals need certified providers.
• A benchmark means nothing without its conditions. The 501 t/s figure is a per-user peak on a single H100, not an aggregate spread across concurrent users. Check the hardware, concurrency, and whether a number is per-user or aggregate before comparing providers.
• Today's leader won't be tomorrow's. H200, Blackwell B200, and GB300 will reset H100 benchmarks within 12 to 18 months, pushing single-user throughput past 1,000 t/s. Pick a provider on the trade-offs that survive each hardware generation, not on whoever holds the speed crown this quarter.

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Why Inference Speed Became a Competitive Moat in 2026

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Three years ago fast responses meant taking under five seconds. Now that speed feels really slow. Real-time voice agents need to respond in under 400 milliseconds. For example when you talk to a voice assistant you want to hear a response away.

Similarly coding assistants that help with autocomplete need to give you the first suggestions in under 200 milliseconds.

When it comes to workflows that involve steps and reasoning the wait time between each step should be just a few tens of milliseconds, not seconds. These fast response times are crucial for a user experience.

The 2026 inference market has bifurcated into two tiers:

• ASIC-class hardware:- Cerebras at 2,344.9 t/s and Groq at 666.4 t/s for Llama 3.1 8B, per Artificial Analysis live benchmarks.
• GPU-based H100 providers:- operating in the 130–501 t/s range.

Within the GPU tier, the variation is almost entirely driven by software, not hardware. That's where this comparison lives.

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Llama 3.1 8B: Why This Model Still Defines the Benchmark

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Model at a Glance

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The 8B model sits at a unique inflection point. It fits entirely on a single H100 80 GB GPU in FP16 without tensor parallelism, making it deployable across every scale — from hyperscale GPU clusters to a single developer workstation.

MLCommons selected Llama 3.1 8B as the model for the MLPerf Inference 5.1 small LLM benchmark in September 2025, establishing it as the industry standard reference point, with official latency thresholds of TTFT ≤ 2 seconds and TPOT ≤ 100 milliseconds for server workloads.

Artificial Analysis currently tracks 13 providers serving Llama 3.1 8B at a median output speed of 155.3 t/s. The fastest provider (Cerebras at 2,344.9 t/s) is over 67× faster than the slowest (DeepInfra at 34.9 t/s). Who runs your Llama 3.1 8B matters as much as which model you choose.

Benchmark Methodology: The Four Metrics That Matter

Four numbers, measured under disclosed conditions:

• Output Throughput. This is how many tokens are generated per second during decoding.
• Time to First Token. This is the time from when you make a request to when you get the first output token. It's really important for how responsive something feels even if the throughput is high.
• Time Per Output Token. This is how long it takes to get each token during decoding. For example if TPOT is 30 milliseconds then you get 33 tokens, per second which is easy to read but if TPOT is 100 milliseconds then the stream starts to feel choppy.
• Cost per 1 Million Output Tokens. This is a business metric that helps compare costs. Keep in mind that throughput and cost do not increase in a line. You should calculate the cost of a model based on how users you actually have, not just the maximum specs.

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Why H100 is the baseline: The reason the H100 is the baseline is that the H100 SXM uses HBM3 which has a speed of 3,350 GB/s. This is a lot faster than the A100 which has a speed of 2,039 GB/s. The H100 SXM is 1.64 times faster than the A100.

This is important because the 8B decode is limited by how the memory can be used. So the H100 SXM has a limit for how much work it can do.

The H100 also does something called FP8 quantization. This means it can handle 2.2 times more tokens per second than FP16. It does this without losing any quality, which is really good when you are working with big models, like this H100.

Head-to-Head: Throughput, Latency, and Cost on H100

Sources: Artificial Analysis (continuous benchmarks), VentureBeat (Simplismart, December 2025), BentoML published benchmark study. BentoML aggregate figures are across 100 concurrent users on A100 hardware; per-user throughput is substantially lower.

‍Simplismart: 501 t/s on H100 — How, and What It Means

Simplismart has consistently recorded 501 tokens per second on Llama 3.1 8B, achieved without any hardware optimization, purely through software on standard H100 hardware.

Simplismart's platform allows teams to push models to production in 3 clicks, with pod spinup time under 500ms and autoscaling based on latency, concurrency, and memory usage, with scale-to-zero on idle traffic.

Setup:

• Hardware: NVIDIA H100, single-GPU, FP8
• Model: Llama 3.1 8B Instruct
• Methodology: Single-request peak (501 t/s) and sustained-median under concurrent load (~350 t/s)
• Source: Artificial Analysis continuous benchmarks; VentureBeat coverage, December 2025

A 500-token response at 350 t/s completes in ~1.4 seconds. At a standard 150 t/s median from competing H100 GPU providers, the same response takes ~3.3 seconds more than 2x slower. Across thousands of API calls per hour, that dead-wait compounds into minutes of pipeline latency.

The Three-Layer Optimization Stack

Simplismart treats inference as a coordinated system, not a single model on a GPU:

• Application Serving Layer:- ML-workload-specific request scheduling, priority queuing, and batching tuned for LLM inference patterns rather than standard web API traffic.
• Infrastructure Layer:- rapid autoscaling and downscaling with model sharding across GPUs. Load spikes don't produce the latency cliffs common in shared multi-tenant systems.
• Model-GPU Interaction Layer:- 28 custom CUDA kernels replacing standard operations in the forward pass, designed to maximize H100 tensor core and memory bandwidth utilization.

The core architectural move is megakernel-style fusion: Simplismart fuses an entire forward pass into a single GPU kernel, eliminating inter-kernel stalls. On its stack, H100 bandwidth utilization rises from under 50% to ~78%, with throughput gains Simplismart measures at 2.5× over vLLM and 1.5× over SGLang on comparable architectures. That's the architectural explanation for the 501 t/s figure, where standard vLLM stacks on identical H100 hardware deliver ~150–200 t/s.

Pricing and Access

Simplismart offers fully transparent, self-serve pricing. Llama 3.1 8B is available via the Model API at $0.13 per 1M tokens with no infrastructure setup required. Dedicated GPU deployments start at $1.20/GPU/hour (T4) up to $4.00/GPU/hour (H100) and $5.20/GPU/hour (H200), with B200 available on request. 

Compliance and Security

Simplismart is certified under ISO 27001 v2022, AICPA SOC 2 Type II, and GDPR. For on-prem and BYOC deployments, Simplismart provides automated compliance validation for HIPAA, with policy-based access control, token-level PHI/PII controls, and deployment in air-gapped and hybrid VPC environments with zero data exposure.

Additional Capabilities

Beyond inference, Simplismart supports fine-tuning with PEFT (LoRA, QLoRA), SFT, RFT, GRPO, and DPO across text, speech, and image models. Built-in observability includes real-time dashboards for latency, throughput, and cluster health, with native support for Grafana, Prometheus, and OpenTelemetry.

Simplismart is an official partner of NVIDIA and is available as a white-labelled inference platform for NVIDIA Cloud Partners (NCPs), with native integration of NVIDIA Inference Microservices (NIM).

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Together AI: The Serverless Incumbent

Llama 3.1 8B is not available on Together AI's Serverless API. Teams wishing to deploy it must use an on-demand Dedicated Endpoint. Dedicated H100 inference on Together AI is priced at $6.49/hour.

Together AI's in-house kernel team built FlashAttention-4, which runs up to 1.3x faster than cuDNN on NVIDIA Blackwell, and recently launched ATLAS (Adaptive Learning Speculator System), delivering up to 4× faster LLM inference on supported models. Their platform supports a broad catalog of open-weight models with fine-tuning available.

Fine-tuning is available on a per-token basis, with LoRA pricing starting at $0.48/1M tokens for models up to 16B parameters.

Best for: Teams that want Together AI's Dedicated Endpoint infrastructure, FlashAttention-4 and ATLAS kernel research, and access to a broad model catalog from a single platform.

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BentoML: Framework Flexibility with Multi-Backend Support

BentoML was acquired by Modular on February 10, 2026, in a strategic product acquisition. BentoML remains Apache 2.0 open source, and the two teams are integrating BentoML's cloud deployment layer with Modular's MAX inference engine and Mojo programming language.

Unlike token-API providers, BentoML does not prescribe a single inference engine. Teams can choose between vLLM, SGLang, LMDeploy, TensorRT-LLM, and Modular's MAX engine, giving full control over the performance-cost trade-off.

BentoML's benchmark study of Llama 3 8B (June 2024, on A100 80GB) found: LMDeploy achieved up to 4,000 t/s aggregate at 100 concurrent users; vLLM delivered 2,300–2,500 t/s aggregate with best-in-class TTFT across all concurrency levels; TensorRT-LLM showed fast TTFT at low concurrency but requires model compilation overhead before deployment.

Note: these figures are from A100 hardware on Llama 3 8B (not Llama 3.1). Per-user throughput at 100-concurrency is substantially lower than aggregate figures. No updated Llama 3.1 H100 benchmark has been published since the Modular acquisition.

BentoCloud bills by GPU-hour and is SOC 2 Type II certified, with BYOC and on-premises support for regulated industries.

Best for: ML engineering teams who need flexibility to pick their inference backend, build multi-model pipelines or RAG systems, and avoid proprietary lock-in — with a roadmap toward hardware portability via Modular's MAX engine.

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Baseten: Infrastructure-First for Custom Models

Baseten's model library lists Llama 3.1 8B Instruct running on TRT-LLM optimization on H100 hardware. Baseten recommends H100 MIG instances for high-throughput deployments of smaller models like Llama 3.1 8B, paired with TensorRT-LLM and a modified Triton Inference Server.

Dedicated deployments are priced by GPU-hour, appropriate for teams with predictable high-volume workloads. Baseten is HIPAA and SOC 2 Type II compliant, and supports custom model deployments with configurable autoscaling.

Best for: Teams deploying custom or fine-tuned models requiring dedicated GPU compute, HIPAA compliance, and engineering control over TensorRT-LLM compilation.

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The Broader 2026 Landscape: ASICs, GPUs, and What Software Can Still Do

Cerebras' wafer-scale engine, a single chip with ~21 PB/s of on-chip SRAM bandwidth that holds entire models in SRAM rather than HBM delivers 2,344.9 t/s for Llama 3.1 8B, 4–6x faster than Groq's 666.4 t/s. ASICs remain premium-priced; the H100 tier is where production economics currently live for most enterprise workloads.

Simplismart's 501 t/s result illustrates the broader thesis: software-layer optimization on commodity H100 can close a significant portion of the gap with specialized hardware without procurement cost or silicon lock-in. Standard vLLM on H100 achieves ~12,500 t/s at maximum batch (high concurrency), but single-user throughput is far lower at 150–200 t/s. 501 t/s is a per-user / per-session peak which is significant at the request level, not an aggregate diluted across hundreds of concurrent users.

These numbers will shift within 12–18 months:

• H200 SXM: ~4.8 TB/s memory bandwidth — 43% above H100
• Blackwell B200: ~4× per-GPU throughput gain over H100 on 70B-class models per MLPerf v4.1
• GB300 (Blackwell Ultra): MLPerf 5.1 projects >18,000 t/s per GPU on Llama 3.1 8B in offline batch mode

Providers migrating to Blackwell will push past 1,000 t/s at single-user production concurrency, resetting current H100 benchmarks. Simplismart already has B200 and H200 available on its platform.

Decision Framework: Matching Provider to Use Case

Choose Simplismart if:

• You need the highest possible H100 throughput — 501 t/s peak, 350 t/s sustained — at any scale
• You want self-serve access: pay-as-you-go API at $0.13/1M tokens or dedicated H100 at $4.00/GPU/hour, no sales process
• You are building multi-step agentic pipelines where per-call latency compounds
• You need BYOC or on-prem deployment with data never leaving your cloud
• You need ISO 27001, SOC 2 Type II, and GDPR compliance
• You operate in Healthcare & Life Sciences, BFSI, Government, or Defense and need HIPAA compliance — supported via on-prem and BYOC deployments with automated compliance validation, air-gapped environments, and zero data exposure
• You also need fine-tuning (LoRA, QLoRA, SFT, GRPO, DPO) and observability (Grafana, Prometheus, OpenTelemetry) in one platform.
• You want access to H100, H200, and B200 hardware from a single provider

Choose Together AI if:

• You want to deploy Llama 3.1 8B on a Dedicated Endpoint with Together AI's FlashAttention-4 and ATLAS inference stack
• You want access to Together AI's broader model catalog and fine-tuning from one platform
• Your workload justifies dedicated GPU-hour pricing at $6.49/hr (H100)

Choose BentoML if:

• You need flexibility to pick between vLLM, LMDeploy, or TensorRT-LLM
• You're building multi-model pipelines, RAG systems, or composable agentic workflows
• You have engineering bandwidth to configure backends and want to avoid proprietary lock-in

Choose Baseten if:

• You're deploying a custom or fine-tuned model with your own weights
• Your industry mandates HIPAA or SOC 2 Type II
• You want dedicated GPU reservations with configurable autoscaling
• You're operating at 10M+ tokens/day where GPU-hour beats per-token billing

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Closing Thoughts

The inference layer is no longer an afterthought. The spread between providers from 130 t/s to 501 t/s on the same H100 hardware represents real differences in user experience, pipeline execution time, and unit economics.

Simplismart's 501 t/s peak and ~350 t/s sustained median is the current high-water mark for GPU-based H100 inference on Llama 3.1 8B, a software-engineering achievement that raises the performance ceiling for enterprise deployments without requiring custom silicon. Together AI remains the pragmatic default for serverless simplicity. BentoML serves teams that need framework flexibility and compliance-grade managed infrastructure. Baseten serves a distinct segment: custom model deployments with compliance requirements and dedicated infrastructure control.

The durable question isn't which provider is fastest today.

Hardware generations change every 12–18 months. Those trade-offs don't.

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Ready to see what 501 t/s looks like on your Llama 3.1 8B workload? Deploy Llama 3.1 8B on Simplismart to get a production-grade inference stack with industry-leading 501 t/s throughput, autoscaling, and flexible deployment across cloud and on-prem environments.

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FAQs

How fast is Simplismart for Llama 3.1 8B inference?
Simplismart runs Llama 3.1 8B at 501 tokens per second peak and around 350 t/s sustained median under concurrent load on a single NVIDIA H100. That makes it the leading GPU-based H100 provider for Llama 3.1 8B throughput, well ahead of standard vLLM stacks that deliver 150 to 200 t/s on identical hardware.

How does Simplismart reach 501 t/s without specialized hardware?
Simplismart achieves 501 t/s through software optimization alone, not custom silicon. Its stack uses 28 custom CUDA kernels and megakernel-style fusion that compresses an entire forward pass into a single GPU kernel, raising H100 bandwidth utilization from under 50% to roughly 78%. Simplismart measures throughput gains of 2.5x over vLLM and 1.5x over SGLang.

How much does Simplismart cost for Llama 3.1 8B?
Simplismart offers Llama 3.1 8B via its Model API at $0.13 per 1M tokens with no infrastructure setup. Dedicated GPU deployments range from $1.20/GPU/hour (T4) to $4.00/GPU/hour (H100) and $5.20/GPU/hour (H200), with B200 available on request. Pricing is fully self-serve with no sales process required.

Is Simplismart compliant for regulated industries?
Yes. Simplismart is certified under ISO 27001 v2022, AICPA SOC 2 Type II, and GDPR. For on-prem and BYOC deployments, it provides automated HIPAA compliance validation with token-level PHI/PII controls, policy-based access control, and air-gapped or hybrid VPC environments with zero data exposure, suitable for Healthcare, BFSI, Government, and Defense.

How does Simplismart compare to Together AI for Llama 3.1 8B?
Simplismart leads on raw H100 throughput at 501 t/s peak with self-serve API pricing from $0.13/1M tokens. Together AI does not offer Llama 3.1 8B on its serverless tier, requiring a Dedicated Endpoint at $6.49/hour, but brings a broader model catalog and its FlashAttention-4 and ATLAS kernel stack. Choose Simplismart for maximum throughput and transparent access, Together AI for catalog breadth.

Does Simplismart support fine-tuning and observability?
Yes. Simplismart supports fine-tuning with PEFT (LoRA, QLoRA), SFT, RFT, GRPO, and DPO across text, speech, and image models. Built-in observability includes real-time dashboards for latency, throughput, and cluster health, with native support for Grafana, Prometheus, and OpenTelemetry, all in a single platform.

Can I deploy Simplismart in my own cloud or on-premises?
Yes. Simplismart supports BYOC and on-prem deployment where data never leaves your environment, including air-gapped and hybrid VPC setups with zero data exposure. Teams can push models to production in 3 clicks, with pod spinup under 500ms and autoscaling that scales to zero on idle traffic.

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