SambaNova Cloud 2026: Fast Llama & DeepSeek Inference Tested

Speed used to come from closed frontier labs and their own inference stacks. That has changed. SambaNova Cloud now serves open-weight giants like Llama and DeepSeek at speeds that make many GPU endpoints feel slow. This week, a major cloud provider announced it is adding SambaNova and Cerebras hardware next to Google TPUs, which makes that speed much easier to buy. If you build agents, coding tools, or anything else where latency adds up across chained calls, benchmark now, before your architecture locks in around a slower provider.

What’s actually new with SambaNova Cloud

The headline is distribution. A large cloud platform is adding SambaNova’s dataflow chips and Cerebras’ wafer-scale systems to its accelerator lineup alongside Google TPUs. Until now, using SambaNova meant signing up for its own API or negotiating an on-prem or dedicated deal. With the hardware inside a mainstream cloud, your existing procurement, billing, IAM, and data-residency agreements can cover it. For many enterprise teams, that was the real blocker, not the technology.

The underlying product hasn’t changed. SambaNova Cloud runs on the company’s Reconfigurable Dataflow Unit (RDU), the SN40L. Its three-tier memory design (on-chip SRAM, HBM, and large DDR capacity) lets one rack hold very large models and switch between them quickly. That’s why SambaNova focuses on the biggest open models, such as Llama 3.x 405B-class models and the 671B-parameter DeepSeek-R1 and V3 family, rather than only small models that fit easily in SRAM. SambaNova has publicly reported DeepSeek-R1 at roughly 200 tokens per second or more, and Llama 70B-class models in the several-hundred tokens-per-second range. Both figures are well above typical GPU-hosted endpoints for those sizes.

The developer API remains OpenAI-compatible, so moving a working app over mostly means changing the base URL, API key, and model name. A rate-limited free tier is still available, along with paid developer and enterprise tiers that offer higher throughput and dedicated capacity.

Why it matters

• Fast inference is no longer a niche vendor decision. When SambaNova and Cerebras appear in a hyperscaler catalog, choosing a fast LLM inference API looks more like choosing an instance type than onboarding a new supplier.
• Agent loops finish far faster. A 10-step tool-using agent that waits 8 seconds per step on a GPU endpoint can finish in a fraction of that time. Users notice, and your throughput per worker improves.
• DeepSeek inference speed stops being the bottleneck. Reasoning models produce long chains of thought. At 200+ tokens per second, a 3,000-token reasoning trace takes about 15 seconds instead of a minute, which makes R1-style models practical in interactive products.
• Open weights become a stronger alternative to closed APIs. Fast Llama and DeepSeek inference closes much of the latency gap with proprietary models, and you keep the option to self-host or switch providers later.
• Groq has real competition. Teams that standardized on Groq for speed now have a credible alternative with broader large-model coverage, which helps on both pricing and reliability.
• Multi-provider routing becomes the default. With three fast-silicon vendors exposing OpenAI-compatible endpoints, routing by model, cost, and current latency is easy to justify.

How to use SambaNova Cloud today

SambaNova’s own API is still the fastest route. The new cloud marketplace listing uses the same OpenAI-compatible request format; only the endpoint and authentication change.

1. Get an API key. Sign up at cloud.sambanova.ai, open the API section, and generate a key. Export it so you never hard-code it:

   export SAMBANOVA_API_KEY="your-key-here"

2. List the models you can access. The catalog changes often, and free-tier accounts don’t always see every model. Check before you hard-code any model IDs:

   curl -s https://api.sambanova.ai/v1/models \
     -H "Authorization: Bearer $SAMBANOVA_API_KEY" | jq '.data[].id'

3. Make a first call with curl. Replace the model name with one from your list:

   curl https://api.sambanova.ai/v1/chat/completions \
     -H "Authorization: Bearer $SAMBANOVA_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "Meta-Llama-3.3-70B-Instruct",
       "messages": [
         {"role": "system", "content": "You are a concise senior engineer."},
         {"role": "user", "content": "Explain idempotency keys in 3 sentences."}
       ],
       "temperature": 0.2,
       "max_tokens": 300
     }'

4. Point the OpenAI Python SDK at SambaNova. Most existing codebases need only this change. Install the SDK:

   pip install openai

   Then set the base URL and key:

   from openai import OpenAI
   import os
   
   client = OpenAI(
       base_url="https://api.sambanova.ai/v1",
       api_key=os.environ["SAMBANOVA_API_KEY"],
   )
   
   resp = client.chat.completions.create(
       model="DeepSeek-R1",
       messages=[{"role": "user", "content": "Is 9.11 bigger than 9.9? Reason briefly."}],
       max_tokens=1024,
   )
   print(resp.choices[0].message.content)

5. Measure tokens per second yourself. Vendor numbers reflect ideal conditions. This streaming script measures time-to-first-token and output speed from your location. Run it several times, at different times of day:

   import os, time
   from openai import OpenAI
   
   client = OpenAI(base_url="https://api.sambanova.ai/v1",
                   api_key=os.environ["SAMBANOVA_API_KEY"])
   
   def bench(model, prompt="Write a 400-word explainer on vector databases."):
       start = time.perf_counter()
       first = None
       chunks = 0
       stream = client.chat.completions.create(
           model=model,
           messages=[{"role": "user", "content": prompt}],
           max_tokens=800,
           stream=True,
           stream_options={"include_usage": True},
       )
       usage = None
       for event in stream:
           if event.choices and event.choices[0].delta.content:
               if first is None:
                   first = time.perf_counter()
               chunks += 1
           if getattr(event, "usage", None):
               usage = event.usage
       end = time.perf_counter()
       out_tokens = usage.completion_tokens if usage else chunks
       gen_time = end - (first or start)
       print(f"{model}: TTFT={(first - start):.2f}s, "
             f"{out_tokens} tokens, {out_tokens / gen_time:.0f} tok/s")
   
   for m in ["Meta-Llama-3.3-70B-Instruct", "DeepSeek-V3-0324", "DeepSeek-R1"]:
       bench(m)

6. Handle free-tier rate limits. The SambaNova free tier caps requests per minute, and each model has its own limits. Expect HTTP 429s when you burst. Wrap calls in exponential backoff before anything reaches users:

   import time, random
   from openai import RateLimitError
   
   def call_with_retry(fn, retries=5):
       for attempt in range(retries):
           try:
               return fn()
           except RateLimitError:
               time.sleep((2 ** attempt) + random.random())
       raise RuntimeError("Rate limited after retries")

7. Add it as a route, not a replacement. If you use LiteLLM or a similar gateway, register SambaNova next to your existing providers so you can fail over and A/B test latency:

   model_list:
     - model_name: fast-llama-70b
       litellm_params:
         model: sambanova/Meta-Llama-3.3-70B-Instruct
         api_key: os.environ/SAMBANOVA_API_KEY
     - model_name: fast-llama-70b
       litellm_params:
         model: groq/llama-3.3-70b-versatile
         api_key: os.environ/GROQ_API_KEY
     - model_name: fast-llama-70b
       litellm_params:
         model: cerebras/llama-3.3-70b
         api_key: os.environ/CEREBRAS_API_KEY

How it compares: SambaNova vs Cerebras vs Groq

All three sell custom silicon that serves open models faster than general-purpose GPUs, but they make different trade-offs. The figures below are approximate, drawn from vendor-published and third-party benchmarks. Your own Llama inference tokens-per-second results will depend on prompt length, concurrency, and region.

Factor	SambaNova Cloud	Cerebras Inference	Groq
Hardware	SN40L RDU (dataflow, tiered memory)	WSE-3 wafer-scale engine	LPU (deterministic, SRAM-heavy)
Llama 70B-class speed	Several hundred tok/s	Often 1,500+ tok/s, the fastest of the three	Roughly 250-300+ tok/s standard; higher with speculative decoding
Very large models (DeepSeek 671B, 400B-class)	Core strength; DeepSeek-R1 at roughly 200+ tok/s	Selective large-model support; varies by catalog	Focuses more on small and mid-size models
Free tier	Yes, rate-limited per model	Yes, daily token allowance	Yes, rate-limited per model
API compatibility	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible
Enterprise deployment	Cloud, dedicated, on-prem racks, now hyperscaler	Cloud, dedicated, now hyperscaler	Cloud and dedicated
Best fit	Big open models, reasoning workloads, multi-model serving	Lowest possible latency on supported models	Mature developer experience, broad ecosystem integrations

Our take: for raw speed on a 70B model, Cerebras usually wins. For full DeepSeek or a frontier-size Llama served fast, SambaNova is the stronger choice. Groq remains the easiest starting point and has the most third-party integrations. In production, don’t pick just one. Route across at least two.

What’s next

Expect the model catalog to keep changing. SambaNova adds new open releases within days, including DeepSeek updates, new Llama generations, and Qwen-family models, and retires older versions just as fast. Production code that hard-codes a model ID needs a fallback, and you should re-check the models endpoint monthly. Also watch whether the hyperscaler listing gets the full catalog on day one or a curated subset, and whether its pricing matches SambaNova’s direct rates or adds a markup.

The bigger shift is pricing. With three fast-silicon vendors competing in the same cloud marketplaces, per-token prices for open models should keep falling, and “fast” will stop being a premium tier. Competition will move to context length, consistent throughput under heavy concurrency, batch and async pricing, and how well each vendor handles tool calling and structured outputs at speed.

Finally, watch reasoning models. Fast inference matters most when a model generates thousands of hidden tokens before answering. As more open reasoning models ship, SambaNova’s ability to serve very large models quickly could matter more than any single leaderboard number.

Frequently Asked Questions

Is SambaNova Cloud free to use?

Yes. The SambaNova free tier is rate-limited per model, typically by requests per minute and per day, and it’s enough to prototype and benchmark. Production traffic calls for a paid developer or enterprise tier, which raises those limits and can add dedicated capacity. Limits change, so check your account dashboard.

How fast is DeepSeek on SambaNova?

SambaNova has publicly reported the full 671B DeepSeek-R1 running at roughly 200 tokens per second or more, which places it among the fastest providers for that model. Real-world DeepSeek inference speed depends on prompt length, output length, and load, so run the benchmark script above from your own region before you commit.

SambaNova vs Cerebras: which should I choose?

Choose Cerebras for the lowest latency on a supported mid-size model such as a 70B Llama. Choose SambaNova for very large open models such as full DeepSeek or 400B-class Llama, or when you need on-prem deployment. Both are now available through a major cloud, so you can test them side by side without new vendor contracts.

Is SambaNova a good Groq alternative?

Yes, especially for larger models. Both use OpenAI-compatible APIs, so switching means changing the base URL and model name. Groq has the more mature ecosystem; SambaNova covers more frontier-size open models. Many teams run both behind a router and fail over between them.

Can I use the OpenAI SDK with SambaNova Cloud?

Yes. Set the base URL to https://api.sambanova.ai/v1, pass your SambaNova API key, and use a SambaNova model ID. Chat completions and streaming work as expected. Support for tool calling and JSON mode varies by model, so test them on yours.

What tokens per second should I expect for Llama?

On SambaNova, small Llama models can exceed 1,000 tokens per second, and 70B-class models typically run in the several-hundred range. Throughput drops under heavy concurrency and with very long prompts, so benchmark with prompts and concurrency that match your actual workload.