Etched
4.5Paid80
Editorial matchup · June 2026
Etched vs Groq: Which AI Tool Is Better in 2026?
Side-by-side comparison of Etched and Groq — pricing, features, and use cases. Reviewed by our editorial team in Jun 2026.
Use-case score 0–2Updated Jun 2026
AI Infrastructure
Groq
4.9Paid430
Etched and Groq represent two distinct bets on specialized AI inference acceleration.
Groq's Language Processing Unit (LPU) is shipping today with proven 300-800 tokens per second throughput on Llama models, backed by NVIDIA's 20 billion licensing deal in December 2025 and deep enterprise deployments at companies including Dropbox and Volkswagen.
Etched's Sohu ASIC claims 500,000 tokens per second across an 8-chip server on Llama 70B, but as of April 2026 has not shipped to production customers—no independent benchmarks exist at shipping batch sizes.
Groq optimizes for deterministic decode latency through SRAM-only weight storage and static scheduling; Sohu bets on pure transformer specialization via fixed-function silicon at 90% FLOPS utilization versus 30-40% on GPUs. The practical choice depends on timeline and architectural constraints.
Groq suits teams needing production inference with sub-300ms latency available now via GroqCloud; Etched targets hyperscalers running pure transformer workloads at massive scale, but only after delivery credibility is established.
Groq also carries NVIDIA backing and integration with NVIDIA Vera Rubin infrastructure, dramatically reducing execution risk compared to Etched's startup supply chain.
Architecturally, both chips solve the memory-bandwidth bottleneck facing GPUs in decode-dominant inference, but Etched is transformer-only forever—if state-space models or hybrid architectures gain share, Sohu loses its value proposition. Groq runs any transformer model without hardware modifications, making it future-resistant for model architecture shifts.
Production-ready, low-latency inference today
Groq
Groq LPU ships in GroqCloud with independent benchmarks confirming 241-800 tokens per second and sub-100ms time-to-first-token. Etched Sohu is not yet available for purchase or rental; claims are unverified at production batch sizes.
Architectural future-proofing
Groq
Groq LPU runs any transformer model via compiler. Etched Sohu cannot run CNNs, RNNs, state-space models, or MoE architectures like DeepSeek V4—a permanent hardware constraint that becomes critical if transformer dominance ends.
Extreme transformer throughput at batch-1
Etched
Etched claims 500,000 tokens per second on Llama 70B with 8 Sohu chips versus 45,000 on 8xB200 GPUs. However, this benchmark is at batch size 1; GPU throughput scales with batching, and Etched has not published batch-32 or batch-256 figures.
Section 01
Best for what
4 use cases scored. Etched wins 0, Groq wins 2.
- EvenPricing value
Neither tool publishes a starting price.
- EvenFree tier
Neither tool offers a free tier or trial.
- GroqUser ratings
Groq averages 4.9 / 5 vs 4.5 / 5 on the other side.
- GroqReview volume
Groq has 196 ratings vs 90 on the other.
Section 02
Pros & cons
Where each tool earns its rating — and where it falls short.
Etched
AI InfrastructurePros
- Transformer-optimized fixed-function silicon claims 90% FLOPS utilization versus 30-40% on GPUs, eliminating instruction-fetch overhead and runtime scheduling latency.
- 8-chip server configuration purportedly matches 160 H100 GPUs in throughput, promising massive capital efficiency for hyperscale transformer serving if claims hold in production.
- 144GB HBM3E per chip provides generous KV-cache headroom for long-context inference without needing hundreds of chips like Groq does.
- TSMC 4nm manufacturing leverages proven process node with established supply chains, avoiding the yield risks of custom foundry partnerships.
- Closed ecosystem allows aggressive co-design: Etched controls compiler, hardware, and deployment stack end-to-end, unlike GPU-based providers constrained by CUDA compatibility.
Cons
- Sohu has not shipped to production customers as of April 2026—20+ months after announcement. No independent benchmarks exist; all throughput claims come from Etched marketing materials and demos.
- Transformer-only bet means the chip is permanently obsolete if state-space models, hybrid architectures, or any non-transformer paradigm gains traction in production. DeepSeek V4 and Qwen3 MoE models already cannot run on Sohu.
- Claimed 500,000 tok/s is measured at batch 1, which overstates advantage versus GPUs at typical serving batch sizes. Etched has not published batch-32 or batch-256 throughput figures needed for fair datacenter comparison.
- Custom compiler and proprietary software stack require total migration away from vLLM, TensorRT-LLM, and SGLang. No migration path exists from 18-year-old CUDA ecosystem; operators must rebuild entire serving infrastructure.
- Supply risk is severe: young startup with no prior shipping silicon, limited organizational infrastructure, and HBM3E memory contention with NVIDIA for TSMC allocation. Single-vendor dependency on hardware from a Series A company.
Etched
AI InfrastructurePros
- Transformer-optimized fixed-function silicon claims 90% FLOPS utilization versus 30-40% on GPUs, eliminating instruction-fetch overhead and runtime scheduling latency.
- 8-chip server configuration purportedly matches 160 H100 GPUs in throughput, promising massive capital efficiency for hyperscale transformer serving if claims hold in production.
- 144GB HBM3E per chip provides generous KV-cache headroom for long-context inference without needing hundreds of chips like Groq does.
- TSMC 4nm manufacturing leverages proven process node with established supply chains, avoiding the yield risks of custom foundry partnerships.
- Closed ecosystem allows aggressive co-design: Etched controls compiler, hardware, and deployment stack end-to-end, unlike GPU-based providers constrained by CUDA compatibility.
Cons
- Sohu has not shipped to production customers as of April 2026—20+ months after announcement. No independent benchmarks exist; all throughput claims come from Etched marketing materials and demos.
- Transformer-only bet means the chip is permanently obsolete if state-space models, hybrid architectures, or any non-transformer paradigm gains traction in production. DeepSeek V4 and Qwen3 MoE models already cannot run on Sohu.
- Claimed 500,000 tok/s is measured at batch 1, which overstates advantage versus GPUs at typical serving batch sizes. Etched has not published batch-32 or batch-256 throughput figures needed for fair datacenter comparison.
- Custom compiler and proprietary software stack require total migration away from vLLM, TensorRT-LLM, and SGLang. No migration path exists from 18-year-old CUDA ecosystem; operators must rebuild entire serving infrastructure.
- Supply risk is severe: young startup with no prior shipping silicon, limited organizational infrastructure, and HBM3E memory contention with NVIDIA for TSMC allocation. Single-vendor dependency on hardware from a Series A company.
Groq
AI InfrastructurePros
- Shipping production hardware today: Groq LPU is available via GroqCloud with 1.9+ million developers and enterprise deployments at Dropbox, Volkswagen, and Riot Games. Independent benchmarks confirm 241-800 tokens per second on Llama models.
- NVIDIA backing eliminates execution risk: 20 billion licensing deal in December 2025 brought core Groq engineering into NVIDIA's Real-Time Inference division; Groq 3 LPU is now part of NVIDIA Vera Rubin. The remaining GroqCloud entity continues operating under new leadership.
- Deterministic execution via static scheduling and plesiosynchronous chip-to-chip protocol delivers sub-100ms time-to-first-token with zero JIT overhead, enabling latency-sensitive applications (voice AI, code completion) impossible on dynamic GPU schedulers.
- Model-agnostic compiler runs any transformer architecture—Llama, Qwen, Mixtral, Mistral—without hardware changes. Unaffected by model architecture shifts or new training paradigms unlike single-architecture ASICs.
- 250 MB to 500 MB SRAM per chip acts as direct weight storage eliminating HBM latency bottleneck. 150 TB/s SRAM bandwidth on Groq 3 LPU is 45x higher per chip than H100 HBM3 bandwidth.
Cons
- Limited SRAM per chip requires 200+ interconnected LPUs to serve a single large model. Running Llama 70B requires approximately 576 LPUs, creating substantial capital and rack-footprint requirements compared to 8 GPUs.
- LPU v1 maxes out at 230 MB SRAM; scaling to long-context or trillion-parameter models forces massive chip arrays with complex tensor parallelism coordination. Groq 3 LPU increases to 500 MB SRAM but still caps single-chip capacity.
- NVIDIA acquisition of Groq means core engineering leadership (Jonathan Ross and 80% of engineers) moved into NVIDIA; remaining GroqCloud entity operates under new management with uncertain long-term roadmap for independent product development.
- Groq specializes in decode-only performance. Prefill (context encoding) remains slower than GPU alternatives; Vera Rubin strategy pairs LPUs with Rubin GPUs for prefill, requiring disaggregated inference architecture and engineering complexity.
- Cannot train models or run non-transformer workloads. Useful only for inference on already-trained language models; no support for computer vision, training, fine-tuning, or experimental architectures.
Section 03
At a glance
Every spec on one page. Live-pulled from each tool's detail page.
Frequently asked
Etched vs Groq FAQs
Quick answers to the questions readers ask before picking between these two.
Is Etched Sohu available to buy or rent today?
No. As of April 2026, Sohu has not shipped to production customers. Etched has demonstrated the chip to investors and run internal benchmarks, but no independent verification exists and pricing has not been disclosed. Groq LPU is available now via GroqCloud with metered pricing on a per-token basis.
Which chip is faster, Etched Sohu or Groq LPU?
Etched claims 500,000 tokens per second on Llama 70B with 8 Sohu chips at batch 1, versus Groq's verified 300-800 tokens per second on single LPU. The comparison is misleading: Etched's figure requires 8 chips and is measured at batch 1; Groq's baseline requires scaling across many chips for large models too. Groq's advantage is lower time-to-first-token (sub-100ms versus unknown for Sohu), which matters more for interactive applications.
Can Groq run mixture-of-experts models like DeepSeek V4?
Yes. Groq's LPU runs any transformer architecture including MoE models. DeepSeek V4 and Qwen3 run on Groq LPU without limitation. Etched Sohu cannot run MoE, SSM, or non-transformer architectures—a permanent hardware constraint.
What happens if transformers are replaced by a new AI architecture?
Groq LPU remains relevant because the compiler is model-agnostic; if a new architecture emerges, Groq's software stack can adapt. Etched Sohu becomes obsolete hardware because the entire chip is hard-wired for transformer ops. Etched has acknowledged this risk explicitly; it is a binary bet that transformers dominate for 5+ years.
Which is cheaper to operate at scale?
Unknown for Etched because Sohu is not yet available and pricing is not disclosed. Groq pricing is available on a metered per-token basis and is generally competitive with or cheaper than GPU cloud at typical throughput. For comparable quality, Groq Llama 70B is typically 20-60% cheaper than GPU cloud on throughput-adjusted basis, but Groq requires hundreds of interconnected chips for large models, creating high CapEx and rack footprint.
Does NVIDIA's acquisition of Groq change the roadmap?
NVIDIA acquired Groq's core technology and engineering team via 20 billion licensing deal in December 2025. Jonathan Ross and 80% of Groq engineers joined NVIDIA's Real-Time Inference division. Groq 3 LPU is now part of NVIDIA Vera Rubin. The remaining GroqCloud entity continues serving customers under new leadership, but core product development is now NVIDIA-controlled. This reduces Groq's independence but eliminates execution risk versus Etched.
Which should I choose if I need inference today?
Groq via GroqCloud. Production-ready, metered pricing, proven performance on open-source models, NVIDIA backing, and no hardware supply risk. Use Groq for latency-sensitive workloads (chat, voice, coding). Use GPU cloud (H100/B200) for diverse workloads, training, and proprietary models. Monitor Etched but do not bet infrastructure on unshipped hardware.
Bottom line
Teams with proven inference workloads should use Groq today. GroqCloud is production-ready with transparent pricing, enterprise support, and independent performance validation.
The LPU's sub-100ms latency unlocks use cases—voice AI, real-time code completion, conversational agents—that GPU infrastructure cannot economically reach. The NVIDIA partnership removes execution risk and ensures long-term platform development.
Etched's Sohu is a 2027-2028 play at earliest: the technology is credible, but capital-intensive hyperscalers and cloud providers must wait for production silicon, independent batch-size benchmarks, and proven supply chain before committing.
If Sohu ships on schedule and meets claimed throughput, it becomes mandatory for teams with pure transformer workloads at petaflop scale. Until then, Groq is the lower-risk, faster inference path.
For enterprises evaluating both: Groq via GroqCloud for immediate latency wins on open-source models; GPU cloud (NVIDIA H100/B200) as the default for diverse workloads, training, and proprietary model access.
Etched should be monitored closely but not adopted until independent production benchmarks exist and pricing is disclosed. The key risk for Etched is architectural: if state-space models, mixture-of-experts variants, or novel architectures displace pure transformers, Sohu becomes obsolete. Groq hedged this risk by supporting any transformer and remaining part of NVIDIA's broader inference ecosystem.
Related matchups
Keep comparing
More ai infrastructure head-to-heads.
vs
Cerebras vs Etched
Read comparison →vs
Etched vs SambaNova
Read comparison →vs
Etched vs Tenstorrent
Read comparison →vs
Etched vs FriendliAI
Read comparison →vs
Cerebras vs Groq
Read comparison →vsGroq vs SambaNova
Read comparison →Sign up for our newsletter
Receive weekly updates so you can stay up-to-date with the world of AI
Sign up for our newsletter
Receive weekly updates so you can stay up-to-date with the world of AI