SGLang vs vLLM: High-Throughput LLM Inference, RadixAttention & Structured Decoding

LLM serving infrastructure in 2026 demands extreme throughput, minimal latency, and robust support for complex generation patterns. This guide dissects two prominent frameworks, SGLang and vLLM, evaluating their architectural paradigms, performance characteristics, and suitability for advanced use cases like multi-turn reasoning and structured output generation. We focus on their core mechanisms: KV cache management, scheduling, and structured decoding capabilities, providing benchmark data on NVIDIA H100 and L4 GPUs.

Architectural Overview

Both SGLang and vLLM aim to maximize GPU utilization by batching requests and optimizing KV cache access. Their fundamental approaches, however, diverge significantly.

vLLM: PagedAttention and Continuous Batching

vLLM introduced PagedAttention, a memory management scheme inspired by virtual memory paging in operating systems. It decouples the logical sequence of KV cache blocks from their physical memory allocation. This mitigates memory fragmentation and allows for efficient sharing of KV cache blocks among different requests, particularly in multi-user scenarios. Continuous batching further enhances throughput by dynamically adding new requests to the batch as soon as GPU resources become available, rather than waiting for a fixed batch size.

SGLang: RadixAttention and Speculative Execution

SGLang builds upon the concept of RadixAttention, which extends KV cache reuse beyond simple prefix sharing. It leverages a radix tree-like structure to manage KV cache blocks, enabling efficient sharing across arbitrary common subsequences, not just prefixes. This is particularly advantageous for multi-turn conversations, agentic loops, and complex prompt engineering where parts of the prompt or previous turns are repeatedly used. SGLang also integrates speculative decoding, where a smaller, faster draft model generates candidate tokens, which are then verified by the larger target model, significantly reducing decoding latency.

KV Cache Management: PagedAttention vs. RadixAttention

The efficiency of KV cache management directly impacts memory utilization and throughput.

PagedAttention Mechanics

In vLLM, the KV cache for each sequence is divided into fixed-size blocks. These blocks are not necessarily contiguous in physical GPU memory. A page table maps logical block IDs to physical block IDs. When a sequence extends, new physical blocks are allocated as needed. If a sequence forks (e.g., beam search), the page table can be copied efficiently, and only new blocks are allocated for divergent paths.

# vLLM PagedAttention conceptual illustration (simplified)
import torch

class PagedAttentionKVManager:
    def __init__(self, block_size, gpu_memory_limit_gb):
        self.block_size = block_size
        self.gpu_memory_limit_bytes = gpu_memory_limit_gb * 1024**3
        self.physical_blocks = {} # {block_id: torch.Tensor}
        self.free_block_ids = set()
        self.next_block_id = 0

    def _allocate_physical_block(self):
        if self.next_block_id in self.physical_blocks:
            raise RuntimeError("Block ID collision, memory management error.")
        # Simulate actual GPU memory allocation
        block_tensor = torch.empty((self.block_size, self.block_size, 2, 128), # K/V, head_dim
                                   dtype=torch.float16, device='cuda')
        self.physical_blocks[self.next_block_id] = block_tensor
        block_id = self.next_block_id
        self.next_block_id += 1
        return block_id

    def get_sequence_blocks(self, sequence_id, num_blocks_needed):
        # In a real system, this would involve a page table lookup
        # and allocation of new blocks if existing ones are full.
        allocated_blocks = []
        for _ in range(num_blocks_needed):
            if not self.free_block_ids:
                # Allocate new physical block if no free ones
                block_id = self._allocate_physical_block()
            else:
                block_id = self.free_block_ids.pop()
            allocated_blocks.append(block_id)
        return allocated_blocks

    def free_sequence_blocks(self, block_ids):
        self.free_block_ids.update(block_ids)
        # In a real system, physical blocks might be truly deallocated
        # or marked as reusable.

# Example usage (conceptual)
# manager = PagedAttentionKVManager(block_size=16, gpu_memory_limit_gb=24)
# seq1_blocks = manager.get_sequence_blocks(sequence_id=1, num_blocks_needed=10)
# seq2_blocks = manager.get_sequence_blocks(sequence_id=2, num_blocks_needed=15)
# manager.free_sequence_blocks(seq1_blocks)

RadixAttention Mechanics

RadixAttention, as implemented in SGLang, organizes KV cache blocks into a radix tree. Each node in the tree represents a common prefix of sequences. When a new sequence arrives, SGLang traverses the tree to find the longest common prefix with existing sequences. It then reuses the KV cache blocks corresponding to this prefix. Only the divergent suffix requires new block allocations. This is particularly powerful for scenarios like:

1. Multi-turn conversations: The initial turns of a conversation can be cached and reused for subsequent turns.
2. Agentic loops: If an agent repeatedly tries different tools or prompts based on a common initial context, the context's KV cache is reused.
3. Complex prompt templates: Prompts with large, static instruction sets can have their KV cache pre-computed and shared.

# SGLang RadixAttention conceptual illustration (simplified)
class RadixTreeNode:
    def __init__(self, token_id=None, kv_cache_block_id=None):
        self.token_id = token_id # Token represented by this node
        self.kv_cache_block_id = kv_cache_block_id # Physical block ID for this token's KV
        self.children = {} # {token_id: RadixTreeNode}
        self.sequences = set() # Set of sequence_ids passing through this node

class RadixAttentionKVManager:
    def __init__(self, block_size):
        self.root = RadixTreeNode()
        self.block_size = block_size
        self.physical_blocks = {} # {block_id: torch.Tensor}
        self.next_block_id = 0

    def _allocate_physical_block(self):
        block_id = self.next_block_id
        self.physical_blocks[block_id] = torch.empty((self.block_size, self.block_size, 2, 128),
                                                     dtype=torch.float16, device='cuda')
        self.next_block_id += 1
        return block_id

    def get_or_create_path(self, sequence_id, tokens):
        current_node = self.root
        path_blocks = []
        for token in tokens:
            if token not in current_node.children:
                # Create new node and allocate KV block
                new_block_id = self._allocate_physical_block()
                current_node.children[token] = RadixTreeNode(token_id=token, kv_cache_block_id=new_block_id)
            current_node = current_node.children[token]
            current_node.sequences.add(sequence_id)
            path_blocks.append(current_node.kv_cache_block_id)
        return path_blocks

    def remove_sequence(self, sequence_id, tokens):
        # Traverse and remove sequence_id from nodes.
        # If a node's sequence set becomes empty and it's not a prefix for others,
        # its KV block can be marked for deallocation/reuse.
        pass # Complex logic for actual tree pruning and block freeing

# Example usage (conceptual)
# manager = RadixAttentionKVManager(block_size=16)
# seq1_tokens = [1, 5, 2, 8]
# seq1_blocks = manager.get_or_create_path(1, seq1_tokens)
# seq2_tokens = [1, 5, 3, 9] # Shares prefix [1, 5]
# seq2_blocks = manager.get_or_create_path(2, seq2_tokens)
# print(f"Seq1 blocks: {seq1_blocks}") # First two blocks might be shared
# print(f"Seq2 blocks: {seq2_blocks}") # First two blocks might be shared

Structured Decoding: XGrammar vs. Outlines

Generating structured output (e.g., JSON, XML, specific formats) is critical for integrating LLMs into programmatic workflows. Both frameworks offer mechanisms for this, but with different underlying approaches.

SGLang: XGrammar

SGLang integrates XGrammar, a powerful and flexible grammar-based constraint system. XGrammar allows users to define output schemas using a syntax similar to EBNF or regular expressions. During generation, SGLang's sampler prunes the vocabulary at each token generation step, ensuring that only tokens that conform to the specified grammar are considered. This guarantees valid structured output and can significantly reduce the number of tokens required for generation by guiding the model more effectively.

# SGLang XGrammar example for JSON output
import sglang as sg

@sg.function
def generate_json_object(s, user_query):
    s += sg.user(user_query)
    s += sg.assistant(
        r'```json' +
        r'{' +
        r'  "name": "' + sg.gen("name", max_tokens=16, stop='"') + r'",' +
        r'  "age": ' + sg.gen("age", max_tokens=4, stop=',') + r',' +
        r'  "city": "' + sg.gen("city", max_tokens=16, stop='"') + r'"' +
        r'}' +
        r'```'
    )

# Example usage (assuming sglang server is running)
# state = sg.State()
# state = generate_json_object(state, "Tell me about a person named Alice, 30 years old, living in New York.")
# print(state["name"])
# print(state["age"])
# print(state["city"])

vLLM: Outlines Integration

vLLM does not natively implement grammar-based sampling but integrates well with libraries like Outlines. Outlines provides a declarative syntax for specifying generation constraints using regular expressions, JSON schemas, or Python types. It then compiles these constraints into a finite state machine (FSM) that guides the token generation process. vLLM's API can be used to pass these FSM-derived constraints to the sampling kernel, ensuring valid output.

# vLLM with Outlines example for JSON output
import outlines
from vllm import LLM, SamplingParams

# 1. Define the JSON schema
json_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "city": {"type": "string"}
    },
    "required": ["name", "age", "city"]
}

# 2. Create a JSON schema guided generator using Outlines
generator = outlines.generate.json(LLM, json_schema) # LLM here is a placeholder for vLLM's model instance

# 3. Define the prompt
prompt = "Generate a JSON object for a person named Bob, 25 years old, living in London."

# 4. Generate with constraints (conceptual, actual integration might vary slightly)
# This part would involve passing the FSM from outlines to vLLm's sampling parameters.
# For vLLM, this typically means using a custom `logits_processor` or similar mechanism
# that Outlines provides to interface with vLLM's sampling.
#
# A more direct integration might look like this with Outlines' vLLM support:
# model = LLM(model="mistralai/Mistral-7B-Instruct-v0.2", trust_remote_code=True)
# generator = outlines.generate.json(model, json_schema)
# result = generator(prompt)
# print(result)

# For a more direct vLLM-only approach (without Outlines, less flexible):
# This would require manually implementing a logits processor.
# class JsonLogitsProcessor:
#     def __call__(self, token_ids: List[int], logits: torch.Tensor) -> torch.Tensor:
#         # Implement logic to mask logits based on expected JSON structure
#         # This is significantly more complex than using Outlines.
#         return logits
#
# sampling_params = SamplingParams(temperature=0.0, top_p=1.0, max_tokens=100,
#                                  logits_processor=[JsonLogitsProcessor()])
# outputs = model.generate(prompt, sampling_params)

Benchmarking: H100 vs. L4 Performance

We conducted benchmarks on NVIDIA H100 (80GB) and L4 (24GB) GPUs to evaluate throughput and latency under varying load conditions. The models used were Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.2.

Setup:

• H100: Single GPU, 80GB VRAM.
• L4: Single GPU, 24GB VRAM.
• Workload: Concurrent requests with varying prompt lengths (50-500 tokens) and generation lengths (50-200 tokens).
• Metrics: Throughput (tokens/second) and P95 latency (seconds).
• Batching: Continuous batching for vLLM, dynamic batching for SGLang.

Throughput (Tokens/sec)

Interpretation: SGLang consistently outperforms vLLM in raw throughput, especially when there's significant KV cache reuse (indicated by "shared" prompt length). RadixAttention's ability to share arbitrary common subsequences provides a tangible advantage in real-world, multi-turn, or agentic workloads. Speculative decoding also contributes to SGLang's higher token generation rate.

P95 Latency (Seconds)

Interpretation: SGLang generally exhibits lower P95 latency. This is attributable to speculative decoding reducing the effective per-token generation time and RadixAttention minimizing KV cache recomputation for common prefixes, leading to faster initial token generation.

Production Gotchas & Troubleshooting

1. KV Cache Memory Exhaustion

• Symptom: CUDA out of memory errors, especially under high concurrency or with long sequences.
• vLLM: PagedAttention helps, but large models and many concurrent long sequences will still exhaust VRAM.
  • Fix:
    • Reduce max_model_len if possible.
    • Decrease gpu_memory_utilization to reserve more host memory for other processes or to allow for more aggressive swapping (though this impacts performance).
    • Upgrade to GPUs with more VRAM (e.g., H100 80GB).
    • Implement request queuing and rate limiting at the application layer.
• SGLang: RadixAttention can reduce memory pressure for specific workloads, but it's not a panacea.
  • Fix: Same as vLLM. Additionally, ensure your application effectively leverages KV cache reuse patterns. If every request is unique, RadixAttention's benefits are diminished. Monitor KV cache hit rates.

2. Structured Decoding Failures

• Symptom: Generated JSON is invalid, or the model gets stuck in a loop.
• SGLang (XGrammar):
  • Failure Mode: Incorrect grammar definition. A subtle error in the EBNF-like syntax can lead to impossible states or unexpected token pruning.
  • Fix:
    • Thoroughly test grammar definitions with simple inputs.
    • Use SGLang's debugging tools (if available) to inspect the grammar state.
    • Ensure the model has been fine-tuned on structured data, as a model unaccustomed to generating structured output might struggle even with strong grammar constraints.
• vLLM (Outlines):
  • Failure Mode: Mismatch between Outlines' FSM and vLLM's sampling. Outlines might generate an FSM that is too restrictive or has edge cases that the underlying model struggles with.
  • Fix:
    • Verify Outlines' FSM generation with a simpler model or by inspecting the FSM state.
    • Ensure the logits_processor correctly applies the FSM constraints without introducing performance bottlenecks.
    • Check for version compatibility between vLLM and Outlines.

3. Suboptimal Throughput/Latency

• Symptom: Benchmarks don't match expected performance, or real-world latency is high.
• Common to both:
  • Failure Mode: Insufficient GPU utilization due to low request concurrency, small batch sizes, or CPU bottlenecks.
  • Fix:
    • Increase concurrent requests to saturate the GPU.
    • Monitor GPU utilization (nvidia-smi). If it's low, your application isn't sending enough requests.
    • Check CPU usage. If the CPU is maxed out, it might be a bottleneck in data transfer or pre/post-processing.
    • Ensure max_batch_size (or equivalent) is configured appropriately.
    • For SGLang, ensure speculative decoding is enabled and configured with an appropriate draft model. A poorly chosen draft model can hurt performance.
  • SGLang Specific:
    • Failure Mode: RadixAttention benefits aren't realized if workloads don't have common prefixes/subsequences.
    • Fix: Analyze your application's prompt patterns. If prompts are highly unique, RadixAttention's overhead might slightly outweigh its benefits. Consider if prompt engineering can introduce more commonalities.

Frequently Asked Questions

1. When should I choose SGLang over vLLM?

Choose SGLang when your workload involves significant KV cache reuse, such as multi-turn conversations, agentic loops, or complex prompt templates with shared instructions. Its RadixAttention and speculative decoding offer superior throughput and lower latency in these scenarios. If structured output generation with strong guarantees is a primary requirement, SGLang's XGrammar is also a compelling feature.

2. Is vLLM still relevant in 2026 given SGLang's advancements?

Absolutely. vLLM remains a highly performant and mature serving framework. For workloads dominated by single-turn, unique requests without extensive KV cache sharing, vLLM's PagedAttention and continuous batching provide excellent baseline performance. Its broader adoption and community support can also be a factor for some teams. The choice depends on the specific characteristics of your LLM application.

3. How does speculative decoding in SGLang impact memory usage?

Speculative decoding requires loading an additional, smaller draft model into GPU memory. This increases the baseline VRAM footprint. However, the performance gains (reduced decoding latency) often outweigh this memory overhead, especially for larger target models where the decoding step is a bottleneck. The draft model is typically much smaller (e.g., 1B-3B parameters) compared to the target model (e.g., 7B-70B parameters).

4. Can I use SGLang's RadixAttention with vLLM's PagedAttention?

No, these are distinct KV cache management implementations within their respective frameworks. They are not designed to be interoperable or combined directly. You choose one framework, and thus one KV cache management strategy, for your serving infrastructure.

5. What are the implications of using structured decoding on inference latency?

Structured decoding, whether via SGLang's XGrammar or vLLM with Outlines, generally increases latency per token compared to unconstrained generation. This is because the sampling process involves additional computation to filter tokens based on grammar rules. However, it significantly reduces the total generation time for valid output by preventing the model from generating invalid tokens and potentially reducing the number of retries or post-processing steps required. The trade-off is valid output guaranteed at a slight per-token cost.